Parkinson’s Disease Analysis

Data Sets

Datasets were searched for using the same filters as before - must be human tissue that expressed lewy bodies, must have at least 3 patients and 3 controls, and must…

10 datasets were identified:

CodeID	Tissue/Cell type	Variant	Samples	Type of array	GEO Ref	Comments	PIs
DIJ	Substantia Nigra	not specified	15 disease, 8 control	Affymetrix Human Genome U133 Plus 2.0 Array	GSE49036	Has some Braak staging	Dijkstra AA, Ingrassia AI, de Menezes RX, van Kesteren RE, Rozemuller AM, Heutink P, vandeBerg WJ
FFR	Substantia Nigra	not specified	9 replicates for the controls and 16 replicates for the Parkinson’s disease patients	Affymetrix Human Genome U133 Plus 2.0 Array	GSE7621		Ffrench-Mullen JM
MOR.SN	substantia nigra, split into medial and lateral portions	sporadic	15 samples of medial parkinsonian SN, 9 samples of lateral parkinsonian SN (24), 8 medial nigra control samples and 7 lateral nigra control samples(15)	Affymetrix Human Genome U133A Array	GSE8397	Age and sex	Moran LB, Graeber MB
MID1	Substantia Nigra pars compacta	not specified	10 PD brain samples and 8 control	Affymetrix Human Genome U133 Plus 2.0 Array	GSE20141		Middleton FA, Kim PD, Zhang-James Y, Davis RL
MID2	whole substantia nigra	not specified	18 controls, 11 patients	Affymetrix Human Genome U133A Array	GSE20292	Age and sex	Middleton FA, James M, Zhang Y, Davis RL
DUM	Cortex (BA9)	not specified	29 PD, 44 neurologically normal controls	Illumina HiSeq 2000	GSE68719	Have age of death, sex, and some samples have corresponding proteomics	Dumitriu A, Golji J, Labadorf AT, Gao B, Beach TG, Myers RH, Longo KA, Latourelle JC
MOR.FC	Frontal cortex	sporadic	3 Controls 5 Patients	Affymetrix Human Genome U133A Array	GSE8397	Age and sex	Moran LB, Graeber MB
LEW	two regions of the medulla	not specified	14 PD samples and 8 controls (two regions)	Affymetrix Human Genome U133A 2.0 Array	GSE19587	Age and sex	lewandowski nm, small sa
MID3	prefrontal cortex	not specified	15 controls 14 patients	Affymetrix Human Genome U133A Array	GSE20168	Age and sex	Middleton FA, James M, Zhang Y, Davis RL
MID4	putamen	not specified	20 controls and 15 patients	Affymetrix Human Genome U133A Array	GSE20291	Age and sex	Middleton FA, James M, Zhang Y, Davis RL

Quality Control - PD_QC.R

Checked data distribution using PCA and boxplots. Some of the datasets have quite mixed samples wereas others separate quite nicely into controls and patients. Boxplots didn’t suggest any samples were outliers.

Differential Gene Expression

As with the TDP-43 data, microarray data was analysed using Limma. The genes were divided into positive and negative fold change. Intersected gene lists can be found in /Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression/FoldChange.

Commonly Upregulated Genes

~~~	~~~	~~~	~~~	~~~	~~~	~~~	~~~	~~~	~~~	~~~
A4GNT	C3	CPED1	FAM46A	HCK	KDM4A	LY6G5C	NRDE2	RBMS2	SMIM14	TNFRSF10B
ABCC6	C3AR1	CPM	FAM60A	HDC	KDM4C	LYVE1	NYX	RCC1	SNAP23	TNFRSF10D
AC012065.7	C8orf60	CPSF3L	FANCE	HERC2P3	KIAA0101	LYZL6	OR1F1	RDH16	SND1-IT1	TNFRSF25
ACP5	C9orf3	CROCC	FAS	HIST1H1T	KIAA0226L	MAF	ORM1	RHBDF2	SOGA1	TNFSF14
ACSBG2	CALML4	CROCCP3	FASLG	HIST1H2AB	KIAA0485	MAFF	OSM	RIBC2	SORBS1	TNN
ACSS3	CASP1	CSPG4	FCGBP	HIST1H2AD	KIAA1614	MAP2K7	P4HA1	RIPK2	SOS2	TP73-AS1
ADH1B	CASP2	CTBP2	FCGR2A	HMOX1	KIAA1661	MAPKBP1	PAGE1	RNASE6	SOX12	TRAF1
ADIPOQ	CASP4	CTD-2269F5.1	FCGR2B	HNRNPM	KIF13A	MAVS	PAPOLA	RP11-217B7.2	SOX2	TRAPPC10
ADORA3	CASP6	CXCR4	FCRL2	HOXB8	KLHL36	MBD5	PARP16	RP11-255C15.3	SOX9	TRIM21
AF007147	CASP7	CXorf21	FGF17	HP1BP3	KLK2	MBTD1	PAWR	RP11-403P17.4	SP140L	TRIM5
AGAP10	CASP8	CXorf36	FGF3	HS3ST1	KRI1	MCM3AP-AS1	PCDH12	RP11-548H18.2	SPG21	TTLL5
AGRP	CATSPER2	CYP21A1P	FGFR1	HSPA1A	KRT19P2	MECOM	PCDHGA8	RPA4	SPINK1	TUBD1
ALOX5	CCDC101	CYP39A1	FKBP10	HSPA1L	L2HGDH	MED13L	PDLIM1	RPL23AP32	SPTLC3	TULP2
ALOX5AP	CCDC15	CYTH4	FLCN	HSPA6	LAG3	METTL4	PELI2	RPL23AP53	SRP19	TYMP
ALPK1	CCDC40	DAPP1	FLJ21369	HSPD1	LAMB2	METTL7A	PGF	RREB1	SRSF5	TYROBP
AMELX	CCL17	DBF4B	FLT1	IFNA2	LAT2	MFAP5	PGPEP1	RRNAD1	SSTR3	UGGT1
AMELY	CCL22	DCLRE1C	FLT3LG	IFNA21	LECT1	MICB	PIDD1	RUNX1-IT1	ST20	UPK3B
AMH	CCL27	DDR1-AS1	FMO5	IGF1R	LEF1	MID1	PIGO	RUNX3	ST6GAL1	USP34
ANGPT2	CD14	DENND2D	FNDC3B	IGFBP5	LENEP	MILR1	PILRA	S100A11P1	STAG3L3	VPS54
ANP32A-IT1	CD163	DFFB	FOXD1	IGKV1-17	LEPREL1	MIR612	PIM1	S100A4	STIP1	VSIG4
AP1G2	CD207	DIP2A	FOXL2	IGLL3P	LEPREL4	MKRN4P	PLAC8	SAFB2	STK10	WBSCR16
APOBEC3C	CD22	DISC1	FPR1	IGSF9B	LGALS9	MS4A6A	PLEKHF2	SAT1	STOM	WDR4
APOC2	CD247	DKFZP434A062	FYB	IL12RB1	LILRA2	MSH5	PLGLB1	SCAF4	TAF4	WDR52
AQP8	CD300A	DMC1	FZD5	IL16	LINC00115	MSX1	PLIN2	SCD5	TAL1	WDR55
ARHGAP25	CD37	DNA2	FZD7	IL17RB	LINC00260	MT1M	PLK3	SCIN	TAS2R10	WDR78
ARHGDIB	CD48	DNAJB1	FZD9	IL18	LINC00894	MYH7B	PLOD2	SCNN1B	TAS2R13	XRCC2
ARHGEF4	CD68	DNAJB6	G0S2	IL1R1	LINC00963	MYO1C	PLTP	SDCCAG3	TAZ	ZBED2
ARID3A	CD84	DNASE2	G3BP1	IL21	LLGL2	MZT2B	PODNL1	SERPINA1	TBX6	ZC3HAV1
ATAD2B	CDA	DOCK10	GABPA	INE1	LOC100272216	NABP1	POGLUT1	SERPINH1	TBXAS1	ZFP36L1
ATHL1	CDC14A	DOCK2	GAL3ST4	INPP5B	LOC100506699	NACAP1	POGZ	SERTAD3	TCF3	ZMYND8
ATP8B1	CDK2AP2	DSE	GAS1	INPP5D	LOC101928198	NBEAL2	PPP1R14D	SH2B2	TFAP2B	ZNF14
ATXN7	CEBPD	DTYMK	GATA1	INVS	LOC101928274	NBR2	PPP2R1B	SHMT1	TFEC	ZNF217
AVIL	CEL	EEF1DP5	GCKR	IRF4	LOC101929240	NCKAP1L	PRR11	SIGLEC7	TFPI	ZNF224
AZGP1	CHD2	EFNA4	GDF15	IRF6	LOC101929889	NEUROG1	PRRG4	SIGLEC8	TGFBR3	ZNF235
AZGP1P1	CHKB-CPT1B	EGF	GDF9	IRF7	LOC102724229	NFATC3	PSCA	SIM2	TGIF1	ZNF34
AZU1	CHRNG	ELF4	GEM	ITFG2	LOC102725016	NFE2	PSG11	SIX6	TIMM44	ZNF354A
BANP	CHST4	EMC10	GJA9-MYCBP	ITGB6	LOC284009	NHLH1	PTPN14	SLA	TLR5	ZNF430
BC069804	CLEC2B	ENTPD1	GJD2	ITPR3	LOC729164	NOTCH2NL	PTPRCAP	SLAMF8	TM4SF1	ZNF446
BMP8B	CLIC2	ENTPD7	GK2	JMJD6	LOC79999	NOX3	RAB13	SLC11A1	TMEM176A	ZNF516
BRPF1	CLK1	EP300	GP9	KCNE4	LPAR2	NPIPA1	RAC2	SLC27A3	TMEM19	ZNF665
BTBD18	COL16A1	EPOR	GPR25	KCNJ8	LPAR4	NPIPB15	RAD52	SLC2A5	TMEM51	ZNF670
C10orf12	COL5A1	ETV1	GRAMD3	KCNK5	LPP	NPL	RAD54L	SLC35C2	TMPRSS11E	ZNF710
C11orf16	COL7A1	FAM106A	GTSE1	KCNQ1	LRRC1	NPR3	RBL1	SLC7A9	TMPRSS5	ZNF721
C1QB	COL9A1	FAM118A	GUSBP3	KCTD5	LRRFIP1	NR6A1	RBM38	SMAD6	TNFAIP8	ZNF74
										ZNF783

Commonly Downregulated Genes

~~~	~~~	~~~	~~~	~~~	~~~	~~~	~~~	~~~	~~~	~~~
ABCA11P	ATP5D	CLINT1	EIF2B3	GPN1	LOC728392	MZT2A	PFDN2	PSMD8	SLC9A1	TSSC1
ABCF1	ATP5F1	CLIP3	EIF2S1	GPX4	LRBA	NAPA	PFKM	PSME3	SLITRK5	TTC19
ABHD11	ATP5G1	CLTB	EIF3F	GRAMD1B	LRPPRC	NDEL1	PFN2	PSMG1	SNCA	TTC9
ACAT1	ATP6AP1	CMAS	EIF3I	GSS	LYRM4	NDFIP1	PGAM1	PTBP2	SNRPN	TUBA1B
ACAT2	ATP6V0B	COA3	EIF3K	GSTA4	LYRM9	NDUFA10	PGRMC1	PTDSS1	SNX24	TUBA1C
ACP2	ATP6V0C	COA7	EIF6	GSTO1	MAGED1	NDUFA13	PHB2	PTP4A1	SORD	TUBB
ACTB	ATP6V1E1	COMMD9	ELOVL6	HARS	MAGED2	NDUFA3	PINK1	PTPRA	SPAG16	TUBB2A
ACTR1A	ATP6V1F	COPS3	ELP3	HERC6	MAK16	NDUFA7	PITPNA	RAB14	SPAG7	TUBB3
ADAM23	ATP6V1G2	COPS4	EMC7	HINT1	MAP1B	NDUFA9	PITPNB	RABEPK	SPCS1	TUBG1
AFG3L2	ATP6V1H	COPS5	ENDOD1	HLTF	MAP1LC3B	NDUFAB1	PITRM1	RAN	SPCS3	TUFM
AGTPBP1	AURKAIP1	COPS7A	ENOPH1	HMCES	MAPK10	NDUFAF1	PKI55	RANGRF	SPRYD7	TUSC2
AHCY	AVPI1	COQ3	ENSA	HMGN4	MDH1	NDUFB5	PLD3	RARS	SRPRB	TXNDC15
AIFM1	B3GNT2	COX7A2L	ENTPD6	HNRNPA0	MDH2	NDUFS1	PLEKHB2	RBFOX2	SRRD	UBB
AIMP2	BABAM1	COX8A	ERCC1	HPCAL4	ME2	NDUFS3	PMPCA	RBM3	SSSCA1	UBE2E3
AJAP1	BECN1	CREB3	ERP29	HSPA12A	ME3	NDUFS5	PMS2	RITA1	ST3GAL5	UBE3B
AK055981	BEND5	CRMP1	EXOSC9	IARS	MECR	NDUFV1	PMS2P1	RNASEH1	STAT4	UCHL1
AK5	BEX4	CRY2	FABP3	IARS2	MFN2	NDUFV2	PMS2P5	RNF10	STRAP	UQCRC1
AKAP12	BFSP1	CSNK2A1	FAF1	IDH3B	MGST3	NEDD8	POLR2C	RPA2	STS	UQCRFS1
AKAP6	BPGM	CSRNP2	FAM127A	IDH3G	MICU1	NEU1	POLR3C	RPH3A	STX12	UQCRQ
AKTIP	BRE	CTNNA2	FAM134C	IDI1	MIEF1	NGFRAP1	POP4	RPP14	STXBP1	UROD
ALAS1	BRF2	CUL2	FAM162A	IMP4	MIF	NHP2	PPFIA2	RPS6KC1	SUMO3	UROS
ALDH1A1	BSN	CX3CL1	FAM168A	INSIG1	MIR21	NIF3L1	PPIA	RRAGA	SYN1	USP7
ANXA2	BTBD3	CXorf40A	FAM206A	IQSEC1	MIR3656	NIPSNAP1	PPME1	RTCB	SYT11	UTP18
ANXA6	C11orf49	CXorf40A	FBXW2	IRGQ	MIR4784	NIT2	PPP1R7	RTF1	TACO1	VAMP7
AP1S1	C12orf10	CXorf40B	FDPS	ITFG1	MIR636	NME1	PPP2CA	RTN4	TCEA2	VLDLR
AP2M1	C1orf216	CYC1	FECH	ITPA	MIR6890	NOP16	PPP2R1A	RWDD2B	TCEAL2	VPS41
AP2S1	C21orf33	CYP2E1	FHIT	ITPR1	MLH1	NRXN3	PPP2R2B	SAMM50	TCEAL4	VPS4B
AP3M2	C3orf62	DCTN3	FIBP	KATNB1	MMP24-AS1	NSDHL	PPP2R5D	SARS	TFCP2	VPS51
AP3S2	C5orf30	DCTN6	FIG4	KHDRBS1	MOCS2	NUDCD3	PPP3CB	SCAMP5	TFPT	WBP11
APEH	C6orf106	DCTPP1	FKBP1B	KIAA0391	MOSPD1	NUDT2	PRC1	SCFD1	TIMM10	WDR44
APEX1	CCBL2	DDA1	FLRT1	KIAA0513	MPI	NUP155	PRKCZ	SDHA	TIMM10B	WDR61
APLP2	CCDC51	DDHD2	FRMPD4	KIAA1279	MRPL15	ODC1	PRMT8	SDHAF1	TIMM8B	WDR7
APMAP	CCK	DDX1	FTO	KIF2A	MRPL35	ODF2	PRPF4	SDHAP1	TMED3	WDR77
AREL1	CCNH	DDX24	FUCA1	KIF3C	MRPL4	OPTN	PRR13	SEC31A	TMEM177	WDYHV1
ARF1	CCSER2	DDX42	GABARAPL1	KIFAP3	MRPS18A	ORC5	PSD3	SEH1L	TMEM208	YWHAZ
ARF3	CDC123	DGUOK	GABBR1	KLHDC4	MRPS22	OSBP	PSMA1	SERINC3	TMEM246	ZDHHC4
ARFGAP2	CDC42	DHRS11	GALNT11	L1CAM	MRPS33	OXCT1	PSMA5	SEZ6L2	TMEM41B	ZDHHC6
ARHGEF9	CDK20	DHRS7B	GARS	LANCL1	MRPS35	OXLD1	PSMB3	SF3A3	TMEM97	ZMYM4
ARL2BP	CDK5	DIABLO	GBAS	LANCL2	MRPS7	PAAF1	PSMB4	SF3B5	TMUB2	ZNF365
ARMC2-AS1	CDK7	DKK3	GGNBP2	LBH	MRTO4	PAFAH1B1	PSMB5	SH3BP5	TOMM20	ZNF593
ARPC1A	CDS2	DLD	GLO1	LCMT1	MTCH1	PARL	PSMB6	SIPA1L1	TOR1AIP2	ZNF629
ARPC5L	CERK	DNAJC8	GLOD4	LDHA	MTCH2	PARN	PSMB7	SLC23A2	TOX4
ASS1	CFL1	DRG1	GLT8D1	LDHB	MTCL1	PCCB	PSMC1	SLC25A11	TP53BP1
ASTN2	CHCHD2	DSTN	GMPR2	LDLRAD4	MTHFD1	PCYOX1L	PSMC2	SLC25A3	TPGS2
ATG14	CHP1	DUSP26	GNB1	LDOC1	MTPAP	PDCL3	PSMC5	SLC25A46	TRAP1
ATP1A1	CHST1	DYNC1H1	GNB5	LETMD1	MTX2	PDHB	PSMD1	SLC25A5	TRAPPC2L
ATP1A3	CIAPIN1	DYNC1I1	GOT1	LINC00094	MX1	PDK2	PSMD11	SLC25A6	TSG101
ATP2B2	CIRBP	DZIP3	GOT2	LMO3	MYH10	PDZD8	PSMD14	SLC30A9	TSPAN3
ATP5A1	CITED1	EAPP	GPD1L	LOC101927673	MYL12B	PEX14	PSMD2	SLC35B1	TSPAN7
ATP5B	CLCN6	EIF2AK1	GPI	LOC645166	MYL5	PEX19	PSMD7	SLC6A1	TSR2

Mouse data

Mouse data was sourced from GEO https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE4758, referenced as CON, however during the analysis I realised that alpha synuclein had a positive log fold change, even though every other dataset reported a negative log fold change for sporadic parkinson’s disease. Looking back at the dataset I realised it was an overexpression model rather than a mutant. Woops!

Alpha synuclein expression

What’s interesting is that canonically, alpha synuclein has been suggested to be overexpressed in sporadic parkinson’s patients.

Filtering out other signals

As was done with the TDP-43 pipeline, I had to find other signals that could be contaminating the sporadic signal. I decided to use the ALS datasets from the TDP-43 analysis to represent “general neurodegeneration”, (C9orf72, sALS, PET, RAV, SOD1, FUS) but actually there was very little overlap:

Common Up (ALS)

ALOX5 ALPK1 STOM MAFF COL7A1

Common Down (ALS)

PMS2P1 ATP6V1G2 INSIG1 RRAGA TPGS2 MTCH2 PPP2CA GLO1 PDHB

In a similar vein I decided to source some sporadic Parkinson’s disease blood - to make sure it was leaving a more tissue specific signal. I found two datasets on GEO - GSE99039 (AMA) and GSE72267 (RON). These were bigger datasets than I usually use - AMA was total 353 samples, RON 59. What I realised was that my filters for the presence calls were still 2. This meant that 24,000 genes/probes passed through filtering which was making huge overlaps. I realised a filter of 2 wasn’t really proportional to the number of samples in AMA, so I increased this to 20 (as the dataset is approximately 10 times larger than the other datasets I have been using).

These two datasets produced a common gene expression signature of approximately 5000 genes, which is quite a lot of concordance. When you compare this to the brain signal, there is an overlap of 88 upregulated and 76 downregulated genes. This is quite a small overlap considering the size of the blood signature.

At this point, the remaining gene list for the sporadic signature is 558 genes (look for Filt_ALS_blood files)

Enrichr

Down http://amp.pharm.mssm.edu/Enrichr/enrich?dataset=3rwmj

Up http://amp.pharm.mssm.edu/Enrichr/enrich?dataset=3rwmr

CON DATA

There was a dataset I labelled “CON” with the GSEnumber GSE4758 that I included for a little while as it was a mouse model of alpha synuclein which I later removed as the research question changed.

Adding LRRK2 Data

Oliver Bandmann mentioned that there is a phenomenon that LRRK2 patients exhibit their disease more closely to sporadic patients than other familial patients. I thought this was interesting and could be a good application of my methdology.

I sourced two datasets with LRRK2 patient data - GSE34516 and GSE23290. These were exon array so had to first be preprocessed by Wenbin in the Affymetrix Console using RMA-sketch. The output was normalised expression data. Mas5Calls didn’t seem to work so I skipped this step.

#### BOT GSE34516 #########
setwd("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Data/")
Data <- read.csv("GSE34516.RMA-GENE-EXTENDED-EC-hg19-na36_LRRK2only.csv", row.names = 1)

genename <- as.data.frame(Data$GeneSymbol)
rownames(genename) <- rownames(Data)
colnames(genename) <- "Gene.Symbol"
#Data is already normalised using RMA-sketch
analysis.name<-"BOT" #Label analysis
expressionMatrix<-Data[,1:6] #takes expression from normalised expression set


Treat<-factor(rep(c("Control", "Patient"),c(4,2)), levels=c("Control", "Patient"))
design<-model.matrix(~Treat)
rownames(design)<-colnames(expressionMatrix)
design

#Conduct statistical analysis of expression
library(limma)
fit<-lmFit(expressionMatrix, design) #linear model fit
fit<-eBayes(fit) 
result<-topTable(fit, coef="TreatPatient", adjust="BH", number=nrow(expressionMatrix)) #"BH" adjust for multiple hypothesis testing
#toptable normally takes top number but this takes all
setwd("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression")
write.csv(result, file = paste(analysis.name, "_result.csv", sep = ""))


result$"Fold Change"<-2^result$logFC 
result$"Fold Change"[result$"Fold Change"<1]<-(-1)/result$"Fold Change"[result$"Fold Change"<1] #converts log fold change into a linear value above or below 0
expressionLinear<-as.data.frame(2^expressionMatrix)
expressionLinear$ProbeSetID<-rownames(expressionLinear)
result<-merge(result, expressionLinear, by = 0) #merge values into one array
result<-merge(result, genename, by.x = "Row.names", by.y = 0)
result<-subset(result, subset=(Gene.Symbol !="---")) #if no gene symbol, discount


setwd(dir = "/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression/")
write.csv(result, file=paste(analysis.name, "_finalresult.csv", sep=""), row.names=FALSE, quote = FALSE)

genesort <- result[order(result$P.Value),]
uniqueresult <- genesort[!duplicated(genesort[,16]),]
write.csv(uniqueresult, file=paste(analysis.name, "rankeduniqueresult.csv", sep=""), row.names=FALSE, quote = FALSE)

#### BOT2 GSE23290 #########
setwd("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Data/")
Data <- read.csv("GSE23290.RMA-GENE-EXTENDED-EC-hg19-na36_LRRK2only.csv", row.names = 1)

genename <- as.data.frame(Data$GeneSymbol)
rownames(genename) <- rownames(Data)
colnames(genename) <- "Gene.Symbol"
#Data is already normalised using RMA-sketch
analysis.name<-"BOT2" #Label analysis
expressionMatrix<-Data[,1:8] #takes expression from normalised expression set


Treat<-factor(rep(c("Control", "Patient"),c(5,3)), levels=c("Control", "Patient"))
design<-model.matrix(~Treat)
rownames(design)<-colnames(expressionMatrix)
design

#Conduct statistical analysis of expression
library(limma)
fit<-lmFit(expressionMatrix, design) #linear model fit
fit<-eBayes(fit) 
result<-topTable(fit, coef="TreatPatient", adjust="BH", number=nrow(expressionMatrix)) #"BH" adjust for multiple hypothesis testing
#toptable normally takes top number but this takes all
setwd("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression")
write.csv(result, file = paste(analysis.name, "_result.csv", sep = ""))


result$"Fold Change"<-2^result$logFC 
result$"Fold Change"[result$"Fold Change"<1]<-(-1)/result$"Fold Change"[result$"Fold Change"<1] #converts log fold change into a linear value above or below 0
expressionLinear<-as.data.frame(2^expressionMatrix)
expressionLinear$ProbeSetID<-rownames(expressionLinear)
result<-merge(result, expressionLinear, by = 0) #merge values into one array
result<-merge(result, genename, by.x = "Row.names", by.y = 0)
result<-subset(result, subset=(Gene.Symbol !="---")) #if no gene symbol, discount


setwd(dir = "/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression/")
write.csv(result, file=paste(analysis.name, "_finalresult.csv", sep=""), row.names=FALSE, quote = FALSE)

genesort <- result[order(result$P.Value),]
uniqueresult <- genesort[!duplicated(genesort[,18]),]
write.csv(uniqueresult, file=paste(analysis.name, "rankeduniqueresult.csv", sep=""), row.names=FALSE, quote = FALSE)

I had to skip out a number of steps because it didn’t require the annotation file. There were about 28000 rows left over which was quite a lot, though approximately 7000 were LOC transcripts so took us down to the number of genes you would expect.

After adding these two datasets to the FoldChange_PD.R script, and filtering out for ALS and sporadic PD blood, there were 85 upregulated and 109 downregulated genes. This still included MSX1, SNCA, UCHL1, and ALDH1A1 from PD Malacards but not PINK1 anymore.

EnrichR UP: http://amp.pharm.mssm.edu/Enrichr/enrich?dataset=3t9ff DOWN: http://amp.pharm.mssm.edu/Enrichr/enrich?dataset=3t9fh

Change of direction

The other datasets I had were fibroblasts from PARK2 and LRRK2 patients as provided by Robin Highly/Oliver Bandmann/Heather Mortiboys. These were exon array datasets which were analysed by Wenbin using the Affymetrix console. When these were added to the filters, the number of common genes was 36, only one of which MSX1 was retained.

To give a little more signal, but to also see if the signal could be recovered, I discluded the blood data (as fibroblast is representative enough of a non-neuronal tissue), leaving 54 genes

DOCK2 RAD52 TNFAIP8 P4HA1 MS4A6A CD300A KLHL36 TRIM5 CD163 CXCR4 RIPK2 PAWR ZNF516 JMJD6 CASP7 MSX1 IGF1R LRRC1 SAFB2 C1QB IL17RB LAMB2 CCDC40 CDA FMO5 DNASE2 NFE2 ZNF217 HMOX1 PARP16 HP1BP3 PGPEP1 CD14 NPIPB15 IMP4 LETMD1 AP1S1 FHIT MECR ATP2B2 DCTPP1 WDYHV1 PSMC2 NRXN3 MRTO4 NDUFS3 SLC6A1 NME1 MRPL15 COPS3 TUBG1 PRC1 BFSP1 IARS

Approximately 10 of these genes have a relatively strong relationship with Parkinson’s or LRRK2 specifically. 32 were directly related according to VarElect, 22 indirectly related (VarElect_Results_cmgreen1-sheffield-ac-uk-20180522-014621619).

The next step was to find the PPI partners of these 54 genes.

PPI

#### ALS PARK2 LRRK2 ####

library(biomaRt)

setwd("/Users/clairegreen/Documents/PhD/TDP-43/TDP-43_Code/Results/PPI_Network/")

PPI <- read.table("iref14_Human_UP_noDup_table_nodash.txt", header = T)
braingenes <- read.csv("Zhang_BrainCelltype_Markers_braingenes.csv", header = T)

DEG_list <- readLines("~/Documents/PhD/Parkinsons/Parkinsons_Code/Results/ALSPARK2LRRK2/removeallALLgenes.txt")

mart <- useMart("ENSEMBL_MART_ENSEMBL",dataset="hsapiens_gene_ensembl", host="www.ensembl.org")
attributes <- listAttributes(mart)
mart_back <- getBM(attributes =c("hgnc_symbol", "uniprotswissprot"), filters="hgnc_symbol", values=DEG_list,  mart=mart)
genelist_Uniprot <- subset(mart_back, !(mart_back$uniprotswissprot == ""))
setwd("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/ALSPARK2LRRK2/")
write.csv(genelist_Uniprot, "martback.csv", row.names = F)

# IDENTIFY MISSING GENES AND FIND UNIPROT CODES FOR THEM. NB SOME GENES MAY NOT BE PROTEIN CODING #

mart_table <- read.csv("martback.csv", header = T) #A table with the uniprot codes for the DEGs
uniprot_gene <- mart_table$uniprotswissprot

DEG_PPI <- subset(PPI, PPI$V1 %in% uniprot_gene | PPI$V2 %in% uniprot_gene)
rownames(DEG_PPI) <- 1:nrow(DEG_PPI)

write.csv(DEG_PPI, "DEG_PPI_ALP.csv", row.names = F)

DEG_PPI <- read.csv("DEG_PPI_ALP.csv")

DEG_PPI <- subset(DEG_PPI, DEG_PPI$Gene1 !="-")
DEG_PPI <- subset(DEG_PPI, DEG_PPI$Gene2 !="-")

write.csv(DEG_PPI, "FinalPDPPI.csv", row.names = F, quote = F)

From this we have a network of 1367 interactions between 1034 proteins (file = PD_DEGPPI.R, DEG_PPI_ALP.csv, ALP_PPIgenes.txt. Gene names used).

Next I had to find the corresonding rows in each of the RNA expression data files ready for the correlation anaylysis. The LRRK2 samples were not included in the coexpression analysis because with only 2 and 3 patients respectively, it would results in rho values of 1/-1 or 1, 0.5, 0, -0.5 or 1, which are difficult to threshold. I also removed the MOR.FC dataset as the distribution of Rho values was abnormally distributed

[]

#### ALP####
#Read in network nodes
DEG_PPI <- readLines("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/ALSPARK2LRRK2/ALP_PPIGenes.txt")

setwd("/users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression/")
#Extract PPI network genes from each dataset#####
LEW <- read.csv("LEWfilteredresult.csv")
rownames(LEW) <- LEW$Gene.Symbol
LEW <- LEW[,28:41]
LEW <- subset(LEW, rownames(LEW) %in% DEG_PPI)

MID3 <- read.csv("MID3filteredresult.csv")
rownames(MID3) <- MID3$Gene.Symbol
MID3 <- MID3[,36:49]
MID3 <- subset(MID3, rownames(MID3) %in% DEG_PPI)

MID4 <- read.csv("MID4filteredresult.csv")
rownames(MID4) <- MID4$Gene.Symbol
MID4 <- MID4[,41:55]
MID4 <- subset(MID4, rownames(MID4) %in% DEG_PPI)

MOR.FC <- read.csv("MOR.FCfilteredresult.csv")
rownames(MOR.FC) <- MOR.FC$Gene.Symbol
MOR.FC <- MOR.FC[,24:28]
MOR.FC <- subset(MOR.FC, rownames(MOR.FC) %in% DEG_PPI)

DIJ <- read.csv("DIJfilteredresult.csv")
rownames(DIJ) <- DIJ$Gene.Symbol
DIJ <- DIJ[,29:43]
DIJ <- subset(DIJ, rownames(DIJ) %in% DEG_PPI)

FFR <- read.csv("FFRfilteredresult.csv")
rownames(FFR) <- FFR$Gene.Symbol
FFR <- FFR[,29:44]
FFR <- subset(FFR, rownames(FFR) %in% DEG_PPI)

MID1 <- read.csv("MID1filteredresult.csv")
rownames(MID1) <- MID1$Gene.Symbol
MID1 <- MID1[,28:37]
MID1 <- subset(MID1, rownames(MID1) %in% DEG_PPI)

MID2 <- read.csv("MID2filteredresult.csv")
rownames(MID2) <- MID2$Gene.Symbol
MID2 <- MID2[,39:49]
MID2 <- subset(MID2, rownames(MID2) %in% DEG_PPI)

MOR.SN <- read.csv("MOR.SNfilteredresult.csv")
rownames(MOR.SN) <- MOR.SN$Gene.Symbol
MOR.SN <- MOR.SN[,36:59]
MOR.SN <- subset(MOR.SN, rownames(MOR.SN) %in% DEG_PPI)

DUM <- read.csv("DUM_UniqueGene_DESeq2.csv")
rownames(DUM) <- DUM$hgnc_symbol
DUM <- DUM[,53:81]
DUM <- subset(DUM, rownames(DUM) %in% DEG_PPI)




#Find the gene names that all datasets have in common
DEG_com <- Reduce(intersect, list(rownames(DIJ), rownames(FFR), 
                                  rownames(LEW),rownames(MID1),rownames(MID2),
                                  rownames(MID3),rownames(MID4),rownames(MOR.FC),
                                  rownames(MOR.SN), rownames(DUM)))

This script resulted in 854 common names. Correlation analysis was conducted on sharc (/shared/hidelab2/user/mdp15cmg/Parkinsons/PD_Cor)

When the correlation relationships were downloaded and analysed, only 59 edges reached a consistent correlation value of over 0.1 or below -0.1 regardless of sign. This increases to 208 when the RNA-seq dataset is removed.

Yet Another New Approach

Talking to Kat we hypothesised that the Parkin Fibroblasts were perhaps driving away the signal too strongly. Also the number of samples was so small that it’s not necessarily giving a representative signal.

To compensate, I decided to go back to the blood samples and use some other samples from the AMA GSE99039 dataset which have familial mutations. There were 5 ATP13A2 cases, 15 Parkin cases, and 12 PINK1 cases. Two of the Parkin cases in the metadata didn’t have corresponding files, so this became 13 Parkin cases.

Limma

##### AMA ATP13A2 #########
setwd("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Data/GSE99039_keepfiles/")

#run program to choose .CEL files from directory
celfiles <- fileBrowser(textToShow = "Choose CEL files", testFun = hasSuffix("[cC][eE][lL]"))
Data<-ReadAffy(filenames=celfiles) #read in files
rmaEset<-rma(Data) #normalise using RMA
analysis.name<-"AMA_ATP13A2" #Label analysis
dataMatrixAll<-exprs(rmaEset) #takes expression from normalised expression set


#mas5call generates presence/absence calls for each probeset
mas5call<-mas5calls(Data)
callMatrixAll<-exprs(mas5call)
colnames(callMatrixAll)<-sub(".CEL", ".mas5-Detection", colnames(callMatrixAll),fixed=TRUE)
colnames(callMatrixAll)<-sub(".cel", ".mas5-Detection", colnames(callMatrixAll),fixed=TRUE)
callMatrixAll<-as.data.frame(callMatrixAll)
callMatrixAll$ProbeSetID<-rownames(callMatrixAll)
countPf<-function(x){
  sum(x=="P")
}

#count how many samples have presence calls
countPl<-apply(callMatrixAll, 1, countPf)
callMatrixAll$ProbeSetID<-rownames(callMatrixAll)
countPdf<-data.frame(ProbeSetID=names(countPl), countP=countPl) 

#read annotation
# USING ANNOTATION FILE (if .csv, convert to .txt using excel)
annotation.file<-"/Users/clairegreen/Documents/PhD/TDP-43/TDP-43_Data/HG-U133_Plus_2.na35.annot.csv/HG-U133_Plus_2.na35_SHORT.annot.txt"
annotation<-read.table(annotation.file, header = TRUE, row.names=NULL, sep="\t", skip=0, stringsAsFactors=F, quote = "", comment.char="!", fill = TRUE, as.is = TRUE)
dim(annotation)
nrow(annotation)
annotation<-subset( annotation, subset=(Gene.Symbol !="---")) #if no gene symbol, discount

# Remove rows in which genes are noted to have negative strand matching probes
idxNegativeStrand<-grep("Negative Strand Matching Probes", annotation$Annotation.Notes)
if(length(idxNegativeStrand)>0)
{
  annotation<-annotation[-idxNegativeStrand,]
}


expressionMatrix<-exprs(rmaEset)
colnames(expressionMatrix)

#this is for matched samples
Treat<-factor(rep(c("Control", "Patient"),c(183,5)), levels=c("Control", "Patient"))
design<-model.matrix(~Treat)
rownames(design)<-colnames(expressionMatrix)
design

#Conduct statistical analysis of expression
library(limma)
fit<-lmFit(expressionMatrix, design) #linear model fit
fit<-eBayes(fit) 
result<-topTable(fit, coef="TreatPatient", adjust="BH", number=nrow(expressionMatrix)) #"BH" adjust for multiple hypothesis testing
#toptable normally takes top number but this takes all
setwd("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression")
write.csv(result, file = paste(analysis.name, "_result.csv", sep = ""))

result$"ProbeSetID"<-rownames(result) #make probeset IDs the row names
head(result$"ProbeSetID") 
result$"Fold Change"<-2^result$logFC 
result$"Fold Change"[result$"Fold Change"<1]<-(-1)/result$"Fold Change"[result$"Fold Change"<1] #converts log fold change into a linear value above or below 0
expressionLinear<-as.data.frame(2^expressionMatrix)
expressionLinear$ProbeSetID<-rownames(expressionLinear)
result<-merge(result, expressionLinear, by.x="ProbeSetID", by.y="ProbeSetID") #merge values into one array
result<-merge(annotation, result, by.x="Probe.Set.ID", by.y="ProbeSetID")
result<-merge(result, countPdf, by.x="Probe.Set.ID", by.y="ProbeSetID")
result$Gene.Symbol <- sapply(strsplit(result$Gene.Symbol,"///"), `[`, 1)
result$Ensembl <- sapply(strsplit(result$Ensembl,"///"), `[`, 1)

setwd(dir = "/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression/")
write.csv(result, file=paste(analysis.name, "_finalresult.csv", sep=""), row.names=FALSE, quote = FALSE)

genesort <- result[order(result$P.Value),]
uniqueresult <- genesort[!duplicated(genesort[,5]),]
write.csv(uniqueresult, file=paste(analysis.name, "rankeduniqueresult.csv", sep=""), row.names=FALSE, quote = FALSE)

uniqueresult<-subset(uniqueresult, Gene.Symbol!="") #removes any probes for which there are no gene symbols
uniqueresult<-subset(uniqueresult, subset=(countP>20)) #only takes results that have at least 2 samples with a presence call for a probe
write.csv(uniqueresult, file=paste(analysis.name, "filteredresult.csv", sep=""), row.names=FALSE, quote = FALSE)

##### AMA PINK1 ##########
setwd("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Data/GSE99039_keepfiles/")

#run program to choose .CEL files from directory
celfiles <- fileBrowser(textToShow = "Choose CEL files", testFun = hasSuffix("[cC][eE][lL]"))
Data<-ReadAffy(filenames=celfiles) #read in files
rmaEset<-rma(Data) #normalise using RMA
analysis.name<-"AMA_PINK1" #Label analysis
dataMatrixAll<-exprs(rmaEset) #takes expression from normalised expression set


#mas5call generates presence/absence calls for each probeset
mas5call<-mas5calls(Data)
callMatrixAll<-exprs(mas5call)
colnames(callMatrixAll)<-sub(".CEL", ".mas5-Detection", colnames(callMatrixAll),fixed=TRUE)
colnames(callMatrixAll)<-sub(".cel", ".mas5-Detection", colnames(callMatrixAll),fixed=TRUE)
callMatrixAll<-as.data.frame(callMatrixAll)
callMatrixAll$ProbeSetID<-rownames(callMatrixAll)
countPf<-function(x){
  sum(x=="P")
}

#count how many samples have presence calls
countPl<-apply(callMatrixAll, 1, countPf)
callMatrixAll$ProbeSetID<-rownames(callMatrixAll)
countPdf<-data.frame(ProbeSetID=names(countPl), countP=countPl) 

#read annotation
# USING ANNOTATION FILE (if .csv, convert to .txt using excel)
annotation.file<-"/Users/clairegreen/Documents/PhD/TDP-43/TDP-43_Data/HG-U133_Plus_2.na35.annot.csv/HG-U133_Plus_2.na35_SHORT.annot.txt"
annotation<-read.table(annotation.file, header = TRUE, row.names=NULL, sep="\t", skip=0, stringsAsFactors=F, quote = "", comment.char="!", fill = TRUE, as.is = TRUE)
dim(annotation)
nrow(annotation)
annotation<-subset( annotation, subset=(Gene.Symbol !="---")) #if no gene symbol, discount

# Remove rows in which genes are noted to have negative strand matching probes
idxNegativeStrand<-grep("Negative Strand Matching Probes", annotation$Annotation.Notes)
if(length(idxNegativeStrand)>0)
{
  annotation<-annotation[-idxNegativeStrand,]
}


expressionMatrix<-exprs(rmaEset)
colnames(expressionMatrix)

#this is for matched samples
Treat<-factor(rep(c("Control", "Patient"),c(183,12)), levels=c("Control", "Patient"))
design<-model.matrix(~Treat)
rownames(design)<-colnames(expressionMatrix)
design

#Conduct statistical analysis of expression
library(limma)
fit<-lmFit(expressionMatrix, design) #linear model fit
fit<-eBayes(fit) 
result<-topTable(fit, coef="TreatPatient", adjust="BH", number=nrow(expressionMatrix)) #"BH" adjust for multiple hypothesis testing
#toptable normally takes top number but this takes all
setwd("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression")
write.csv(result, file = paste(analysis.name, "_result.csv", sep = ""))

result$"ProbeSetID"<-rownames(result) #make probeset IDs the row names
head(result$"ProbeSetID") 
result$"Fold Change"<-2^result$logFC 
result$"Fold Change"[result$"Fold Change"<1]<-(-1)/result$"Fold Change"[result$"Fold Change"<1] #converts log fold change into a linear value above or below 0
expressionLinear<-as.data.frame(2^expressionMatrix)
expressionLinear$ProbeSetID<-rownames(expressionLinear)
result<-merge(result, expressionLinear, by.x="ProbeSetID", by.y="ProbeSetID") #merge values into one array
result<-merge(annotation, result, by.x="Probe.Set.ID", by.y="ProbeSetID")
result<-merge(result, countPdf, by.x="Probe.Set.ID", by.y="ProbeSetID")
result$Gene.Symbol <- sapply(strsplit(result$Gene.Symbol,"///"), `[`, 1)
result$Ensembl <- sapply(strsplit(result$Ensembl,"///"), `[`, 1)

setwd(dir = "/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression/")
write.csv(result, file=paste(analysis.name, "_finalresult.csv", sep=""), row.names=FALSE, quote = FALSE)

genesort <- result[order(result$P.Value),]
uniqueresult <- genesort[!duplicated(genesort[,5]),]
write.csv(uniqueresult, file=paste(analysis.name, "rankeduniqueresult.csv", sep=""), row.names=FALSE, quote = FALSE)

uniqueresult<-subset(uniqueresult, Gene.Symbol!="") #removes any probes for which there are no gene symbols
uniqueresult<-subset(uniqueresult, subset=(countP>20)) #only takes results that have at least 2 samples with a presence call for a probe
write.csv(uniqueresult, file=paste(analysis.name, "filteredresult.csv", sep=""), row.names=FALSE, quote = FALSE)

##### AMA PARKIN ##########
setwd("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Data/GSE99039_keepfiles/")

#run program to choose .CEL files from directory
celfiles <- fileBrowser(textToShow = "Choose CEL files", testFun = hasSuffix("[cC][eE][lL]"))
Data<-ReadAffy(filenames=celfiles) #read in files
rmaEset<-rma(Data) #normalise using RMA
analysis.name<-"AMA_PARKIN" #Label analysis
dataMatrixAll<-exprs(rmaEset) #takes expression from normalised expression set


#mas5call generates presence/absence calls for each probeset
mas5call<-mas5calls(Data)
callMatrixAll<-exprs(mas5call)
colnames(callMatrixAll)<-sub(".CEL", ".mas5-Detection", colnames(callMatrixAll),fixed=TRUE)
colnames(callMatrixAll)<-sub(".cel", ".mas5-Detection", colnames(callMatrixAll),fixed=TRUE)
callMatrixAll<-as.data.frame(callMatrixAll)
callMatrixAll$ProbeSetID<-rownames(callMatrixAll)
countPf<-function(x){
  sum(x=="P")
}

#count how many samples have presence calls
countPl<-apply(callMatrixAll, 1, countPf)
callMatrixAll$ProbeSetID<-rownames(callMatrixAll)
countPdf<-data.frame(ProbeSetID=names(countPl), countP=countPl) 

#read annotation
# USING ANNOTATION FILE (if .csv, convert to .txt using excel)
annotation.file<-"/Users/clairegreen/Documents/PhD/TDP-43/TDP-43_Data/HG-U133_Plus_2.na35.annot.csv/HG-U133_Plus_2.na35_SHORT.annot.txt"
annotation<-read.table(annotation.file, header = TRUE, row.names=NULL, sep="\t", skip=0, stringsAsFactors=F, quote = "", comment.char="!", fill = TRUE, as.is = TRUE)
dim(annotation)
nrow(annotation)
annotation<-subset( annotation, subset=(Gene.Symbol !="---")) #if no gene symbol, discount

# Remove rows in which genes are noted to have negative strand matching probes
idxNegativeStrand<-grep("Negative Strand Matching Probes", annotation$Annotation.Notes)
if(length(idxNegativeStrand)>0)
{
  annotation<-annotation[-idxNegativeStrand,]
}


expressionMatrix<-exprs(rmaEset)
colnames(expressionMatrix)

#this is for matched samples
Treat<-factor(rep(c("Control", "Patient"),c(183,13)), levels=c("Control", "Patient"))
design<-model.matrix(~Treat)
rownames(design)<-colnames(expressionMatrix)
design

#Conduct statistical analysis of expression
library(limma)
fit<-lmFit(expressionMatrix, design) #linear model fit
fit<-eBayes(fit) 
result<-topTable(fit, coef="TreatPatient", adjust="BH", number=nrow(expressionMatrix)) #"BH" adjust for multiple hypothesis testing
#toptable normally takes top number but this takes all
setwd("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression")
write.csv(result, file = paste(analysis.name, "_result.csv", sep = ""))

result$"ProbeSetID"<-rownames(result) #make probeset IDs the row names
head(result$"ProbeSetID") 
result$"Fold Change"<-2^result$logFC 
result$"Fold Change"[result$"Fold Change"<1]<-(-1)/result$"Fold Change"[result$"Fold Change"<1] #converts log fold change into a linear value above or below 0
expressionLinear<-as.data.frame(2^expressionMatrix)
expressionLinear$ProbeSetID<-rownames(expressionLinear)
result<-merge(result, expressionLinear, by.x="ProbeSetID", by.y="ProbeSetID") #merge values into one array
result<-merge(annotation, result, by.x="Probe.Set.ID", by.y="ProbeSetID")
result<-merge(result, countPdf, by.x="Probe.Set.ID", by.y="ProbeSetID")
result$Gene.Symbol <- sapply(strsplit(result$Gene.Symbol,"///"), `[`, 1)
result$Ensembl <- sapply(strsplit(result$Ensembl,"///"), `[`, 1)

setwd(dir = "/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression/")
write.csv(result, file=paste(analysis.name, "_finalresult.csv", sep=""), row.names=FALSE, quote = FALSE)

genesort <- result[order(result$P.Value),]
uniqueresult <- genesort[!duplicated(genesort[,5]),]
write.csv(uniqueresult, file=paste(analysis.name, "rankeduniqueresult.csv", sep=""), row.names=FALSE, quote = FALSE)

uniqueresult<-subset(uniqueresult, Gene.Symbol!="") #removes any probes for which there are no gene symbols
uniqueresult<-subset(uniqueresult, subset=(countP>20)) #only takes results that have at least 2 samples with a presence call for a probe
write.csv(uniqueresult, file=paste(analysis.name, "filteredresult.csv", sep=""), row.names=FALSE, quote = FALSE)

Common Differentially Expressed Genes (Blood Edition)

#############################################################
################### FOLD CHANGE DEG #########################
#############################################################

setwd("/users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression/")

LEW <- read.csv("LEWfilteredresult.csv")
LEW <- LEW[order(LEW$P.Value),]
MID3 <- read.csv("MID3filteredresult.csv")
MID3 <- MID3[order(MID3$P.Value),]
MID4 <- read.csv("MID4filteredresult.csv")
MID4 <- MID4[order(MID4$P.Value),]
MOR.FC <- read.csv("MOR.FCfilteredresult.csv")
MOR.FC <- MOR.FC[order(MOR.FC$P.Value),]
DIJ <- read.csv("DIJfilteredresult.csv")
DIJ <- DIJ[order(DIJ$P.Value),]
FFR <- read.csv("FFRfilteredresult.csv")
FFR <- FFR[order(FFR$P.Value),]
MID1 <- read.csv("MID1filteredresult.csv")
MID1 <- MID1[order(MID1$P.Value),]
MID2 <- read.csv("MID2filteredresult.csv")
MID2 <- MID2[order(MID2$P.Value),]
MOR.SN <- read.csv("MOR.SNfilteredresult.csv")
MOR.SN <- MOR.SN[order(MOR.SN$P.Value),]
DUM <- read.csv("DUM_UniqueGene_DESeq2.csv")
DUM <- DUM[order(DUM$pvalue),]
BOT <- read.csv("BOTrankeduniqueresult.csv")
BOT <- BOT[order(BOT$P.Value),]
BOT2 <- read.csv("BOT2rankeduniqueresult.csv")
BOT2 <- BOT2[order(BOT2$P.Value),]




thresh <- 1

upLEW <- subset(LEW, LEW$Fold.Change >= thresh)
upLEWgene <- upLEW$Gene.Symbol

upMID3<- subset(MID3, MID3$Fold.Change >= thresh)
upMID3gene <- upMID3$Gene.Symbol

upMID4 <- subset(MID4, MID4$Fold.Change >= thresh)
upMID4gene <- upMID4$Gene.Symbol

upMOR.FC <- subset(MOR.FC, MOR.FC$Fold.Change >= thresh)
upMOR.FCgene <- upMOR.FC$Gene.Symbol

upDUM <- subset(DUM, DUM$log2FoldChange >= 0)
upDUMgene <- upDUM$hgnc_symbol

upDIJ <- subset(DIJ, DIJ$Fold.Change >= thresh)
upDIJgene <- upDIJ$Gene.Symbol

upFFR<- subset(FFR, FFR$Fold.Change >= thresh)
upFFRgene <- upFFR$Gene.Symbol

upMID1<- subset(MID1, MID1$Fold.Change >= thresh)
upMID1gene <- upMID1$Gene.Symbol

upMID2 <- subset(MID2, MID2$Fold.Change >= thresh)
upMID2gene <- upMID2$Gene.Symbol

upMOR.SN <- subset(MOR.SN, MOR.SN$Fold.Change >= thresh)
upMOR.SNgene <- upMOR.SN$Gene.Symbol

upBOT <- subset(BOT, BOT$Fold.Change >= thresh)
upBOTgene <- upBOT$Gene.Symbol

upBOT2 <- subset(BOT2, BOT2$Fold.Change >= thresh)
upBOT2gene <- upBOT2$Gene.Symbol


INTUP <- Reduce(intersect, list(upLEWgene, upMID3gene, upMID4gene, upMOR.FCgene,
                                upDIJgene, upFFRgene, upMID1gene, upMID2gene, upMOR.SNgene, upDUMgene, upBOTgene, upBOT2gene))



#### DOWN ####
thresh <- -1

downLEW <- subset(LEW, LEW$Fold.Change <= thresh)
downLEWgene <- downLEW$Gene.Symbol

downMID3 <- subset(MID3, MID3$Fold.Change <= thresh)
downMID3gene <- downMID3$Gene.Symbol

downMID4 <- subset(MID4, MID4$Fold.Change <= thresh)
downMID4gene <- downMID4$Gene.Symbol

downMOR.FC <- subset(MOR.FC, MOR.FC$Fold.Change <= thresh)
downMOR.FCgene <- downMOR.FC$Gene.Symbol

downDUM <- subset(DUM, DUM$log2FoldChange <= 0)
downDUMgene <- downDUM$hgnc_symbol

downDIJ <- subset(DIJ, DIJ$Fold.Change <= thresh)
downDIJgene <- downDIJ$Gene.Symbol

downFFR<- subset(FFR, FFR$Fold.Change <= thresh)
downFFRgene <- downFFR$Gene.Symbol

downMID1<- subset(MID1, MID1$Fold.Change <= thresh)
downMID1gene <- downMID1$Gene.Symbol

downMID2 <- subset(MID2, MID2$Fold.Change <= thresh)
downMID2gene <- downMID2$Gene.Symbol

downMOR.SN <- subset(MOR.SN, MOR.SN$Fold.Change <= thresh)
downMOR.SNgene <- downMOR.SN$Gene.Symbol


downBOT <- subset(BOT, BOT$Fold.Change <= thresh)
downBOTgene <- downBOT$Gene.Symbol

downBOT2 <- subset(BOT2, BOT2$Fold.Change <= thresh)
downBOT2gene <- downBOT2$Gene.Symbol

INTDOWN <- Reduce(intersect, list(downLEWgene, downMID3gene, downMID4gene, downMOR.FCgene,
                                  downDIJgene, downFFRgene, downMID1gene, downMID2gene, 
                                  downMOR.SNgene, downDUMgene, downBOTgene, downBOT2gene))




########################### COMMON GENES ##############################
all <- c(INTUP, INTDOWN)
setwd("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression/FoldChange")
# write.table(INTUP,"Sp_LRRK2_upDEGs.txt", row.names = F, col.names = F, quote = F)
# write.table(INTDOWN,"Sp_LRRK2_downDEGs.txt", row.names = F, col.names = F, quote = F)
# write.table(all, "Sp_LRRK2_allDEGs.txt", row.names = F, col.names = F, quote = F)

PDgenes <- readLines("/Users/clairegreen/Documents/PhD/Parkinsons/ParkinsonsDiseaseMalacards.txt")

intersect(INTUP, PDgenes)
intersect(INTDOWN, PDgenes)



####### ALS signature ##########

setwd("/users/clairegreen/Documents/PhD/TDP-43/TDP-43_Code/Results/GeneExpression/noMedian/")

C9 <- read.csv("C9_unique.csv")
C9 <- C9[order(C9$P.Value),]
sals <- read.csv("sals_unique.csv")
sals <- sals[order(sals$P.Value),]

setwd("/users/clairegreen/Documents/PhD/TDP-43/TDP-43_Code/Results/GeneExpression/TDP-43_DEseq2/")

pet <- read.csv("PET_results_keepfiltering.csv")
rav <- read.csv("RAV_results_keepfiltering.csv")

setwd("/users/clairegreen/Documents/PhD/TDP-43/TDP-43_Code/Results/GeneExpression/non-TDP/")

FUS <- read.csv("FUSrankeduniqueresult.csv")
FUS <- FUS[order(FUS$P.Value),]
SOD1 <- read.csv("SOD1rankeduniqueresult.csv")
SOD1 <- SOD1[order(SOD1$P.Value),]


thresh <- 1

upC9 <- subset(C9, C9$Fold.Change >= thresh)
upC9gene <- upC9$Gene.Symbol

upSALS <- subset(sals, sals$Fold.Change >= thresh)
upSALSgene <- upSALS$Gene.Symbol

upPET <- subset(pet, pet$FoldChange >= thresh)
upPETgene <- upPET$hgnc_symbol

upRAV <- subset(rav, rav$FoldChange >= thresh)
upRAVgene <- upRAV$hgnc_symbol

upFUS <- subset(FUS, FUS$Fold.Change >= thresh)
upFUSgene <- upFUS$Gene.Symbol

upSOD1 <- subset(SOD1, SOD1$Fold.Change >= thresh)
upSOD1gene <- upSOD1$Gene.Symbol

INTUP_ALS <- Reduce(intersect, list(upC9gene, upSALSgene, upPETgene, upRAVgene, upFUSgene, upSOD1gene))


#### DOWN 
thresh <- -1

downC9 <- subset(C9, C9$Fold.Change <= thresh)
downC9gene <- downC9$Gene.Symbol

downSALS <- subset(sals, sals$Fold.Change <= thresh)
downSALSgene <- downSALS$Gene.Symbol

downPET <- subset(pet, pet$FoldChange <= thresh)
downPETgene <- downPET$hgnc_symbol

downRAV <- subset(rav, rav$FoldChange <= thresh)
downRAVgene <- downRAV$hgnc_symbol

downFUS <- subset(FUS, FUS$Fold.Change <= thresh)
downFUSgene <- downFUS$Gene.Symbol

downSOD1 <- subset(SOD1, SOD1$Fold.Change <= thresh)
downSOD1gene <- downSOD1$Gene.Symbol

INTDOWN_ALS <- Reduce(intersect, list(downC9gene, downSALSgene, downPETgene, downRAVgene, downFUSgene, downSOD1gene))



##### COMMON GENES ###
upremove <- Reduce(intersect, list (INTUP, INTUP_ALS))
downremove <- Reduce(intersect, list(INTDOWN, INTDOWN_ALS))



###### REMOVE COMMON GENES ###
resultsup <- subset(INTUP, !(INTUP %in% upremove))
resultsdown <- subset(INTDOWN, !(INTDOWN %in% downremove))
results <- c(resultsup, resultsdown)

####### sPD Blood signature ##########

setwd("/users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression/")

AMA <- read.csv("AMAfilteredresult.csv")
AMA <- AMA[order(AMA$P.Value),]
RON <- read.csv("RONfilteredresult.csv")
RON <- RON[order(RON$P.Value),]


thresh <- 1

upAMA <- subset(AMA, AMA$Fold.Change >= thresh)
upAMAgene <- upAMA$Gene.Symbol

upRON <- subset(RON, RON$Fold.Change >= thresh)
upRONgene <- upRON$Gene.Symbol


INTUP_blood <- Reduce(intersect, list(upAMAgene, upRONgene))


#### DOWN ###
thresh <- -1

downAMA <- subset(AMA, AMA$Fold.Change <= thresh)
downAMAgene <- downAMA$Gene.Symbol

downRON <- subset(RON, RON$Fold.Change <= thresh)
downRONgene <- downRON$Gene.Symbol


INTDOWN_blood <- Reduce(intersect, list(downAMAgene, downRONgene))



##### COMMON GENES ###
upremove2 <- Reduce(intersect, list(INTUP, INTUP_blood))
downremove2 <- Reduce(intersect, list(INTDOWN, INTDOWN_blood))



##### REMOVE COMMON GENES ###
resultsup <- subset(resultsup, !(resultsup %in% upremove2))
resultsdown <- subset(resultsdown, !(resultsdown %in% downremove2))
results <- c(resultsup, resultsdown)




setwd("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression/FoldChange/")
# write.table(resultsup, "Sp_LRRK2_FiltALSPDblood_UPgenes.txt", quote = F, row.names = F, col.names = F)
# write.table(resultsdown, "Sp_LRRK2_FiltALSPDblood_DOWNgenes.txt", quote = F, row.names = F, col.names = F)
# write.table(results, "Sp_LRRK2_FiltALSPDblood_ALLgenes.txt", quote = F, row.names = F, col.names = F)
# cat(resultsup, sep="\n")

intersect(resultsup, PDgenes)
intersect(resultsdown, PDgenes)


####### fPD Blood signature ##########

setwd("/users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression/")

AMA_A <- read.csv("AMA_ATP13A2filteredresult.csv")
AMA_A <- AMA_A[order(AMA_A$P.Value),]
AMA_PARK <- read.csv("AMA_PARKINfilteredresult.csv")
AMA_PARK <- AMA_PARK[order(AMA_PARK$P.Value),]
AMA_PINK <- read.csv("AMA_PINK1filteredresult.csv")
AMA_PINK <- AMA_PINK[order(AMA_PINK$P.Value),]


thresh <- 1

upAMA_A <- subset(AMA_A, AMA_A$Fold.Change >= thresh)
upAMA_Agene <- AMA_A$Gene.Symbol

upAMA_PARK<- subset(AMA_PARK, AMA_PARK$Fold.Change >= thresh)
upAMA_PARKgene <- upAMA_PARK$Gene.Symbol

upAMA_PINK<- subset(AMA_PINK, AMA_PINK$Fold.Change >= thresh)
upAMA_PINKgene <- upAMA_PINK$Gene.Symbol


INTUP_fam_blood <- Reduce(intersect, list(upAMA_Agene, upAMA_PARKgene, upAMA_PINKgene))


#### DOWN ###
thresh <- -1

downAMA_A <- subset(AMA_A, AMA_A$Fold.Change <= thresh)
downAMA_Agene <- AMA_A$Gene.Symbol

downAMA_PARK<- subset(AMA_PARK, AMA_PARK$Fold.Change <= thresh)
downAMA_PARKgene <- downAMA_PARK$Gene.Symbol

downAMA_PINK<- subset(AMA_PINK, AMA_PINK$Fold.Change <= thresh)
downAMA_PINKgene <- downAMA_PINK$Gene.Symbol


INTDOWN_fam_blood <- Reduce(intersect, list(downAMA_Agene, downAMA_PARKgene, downAMA_PINKgene))



##### COMMON GENES ###
upremove3 <- Reduce(intersect, list(INTUP, INTUP_fam_blood))
downremove3 <- Reduce(intersect, list(INTDOWN, INTDOWN_fam_blood))



##### REMOVE COMMON GENES ###
resultsup <- subset(resultsup, !(resultsup %in% upremove3))
resultsdown <- subset(resultsdown, !(resultsdown %in% downremove3))
results <- c(resultsup, resultsdown)




setwd("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/FamilialBlood/")
write.table(resultsup, "ALS_sfblood_UPgenes.txt", quote = F, row.names = F, col.names = F)
write.table(resultsdown, "ALS_sfblood_DOWNgenes.txt", quote = F, row.names = F, col.names = F)
write.table(results, "ALS_sfblood_ALLgenes.txt", quote = F, row.names = F, col.names = F)
cat(resultsup, sep="\n")

intersect(resultsup, PDgenes)
intersect(resultsdown, PDgenes)

This produced 170 commonly differentially expressed genes (ALS_sfblood_ALLgenes.txt). These genes included Malacards SCNA, MSX1, UCHL1, ALDH1A1 .

Protein Interaction Expansion

The 170 gene seed created a network of 2159 proteins (/familialblood/PPIgenes). EnrichR (http://amp.pharm.mssm.edu/Enrichr/enrich?dataset=3ztlg) shows high enrichment for a number of processes, making it hard to know exactly what the geneset represents. However for GO Cellular component the highest enrichments are still for the mitochondria

In this case, I am somewhat confident that I am looking in the right area. The only way we will know if it is, however, is to look at whether there is common coexpression

Common Coexpression in LRRK2/Sporadic PD

#### Parkinson's DEG PPI Correlation ####

#Read in network nodes

#### Familial Blood ####
#Read in network nodes
DEG_PPI <- readLines("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/FamilialBlood/PPIGenes.txt")

setwd("/users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/GeneExpression/")
#Extract PPI network genes from each dataset#####
LEW <- read.csv("LEWfilteredresult.csv")
rownames(LEW) <- LEW$Gene.Symbol
LEW <- LEW[,28:41]
LEW <- subset(LEW, rownames(LEW) %in% DEG_PPI)

MID3 <- read.csv("MID3filteredresult.csv")
rownames(MID3) <- MID3$Gene.Symbol
MID3 <- MID3[,36:49]
MID3 <- subset(MID3, rownames(MID3) %in% DEG_PPI)

MID4 <- read.csv("MID4filteredresult.csv")
rownames(MID4) <- MID4$Gene.Symbol
MID4 <- MID4[,41:55]
MID4 <- subset(MID4, rownames(MID4) %in% DEG_PPI)

MOR.FC <- read.csv("MOR.FCfilteredresult.csv")
rownames(MOR.FC) <- MOR.FC$Gene.Symbol
MOR.FC <- MOR.FC[,24:28]
MOR.FC <- subset(MOR.FC, rownames(MOR.FC) %in% DEG_PPI)

DIJ <- read.csv("DIJfilteredresult.csv")
rownames(DIJ) <- DIJ$Gene.Symbol
DIJ <- DIJ[,29:43]
DIJ <- subset(DIJ, rownames(DIJ) %in% DEG_PPI)

FFR <- read.csv("FFRfilteredresult.csv")
rownames(FFR) <- FFR$Gene.Symbol
FFR <- FFR[,29:44]
FFR <- subset(FFR, rownames(FFR) %in% DEG_PPI)

MID1 <- read.csv("MID1filteredresult.csv")
rownames(MID1) <- MID1$Gene.Symbol
MID1 <- MID1[,28:37]
MID1 <- subset(MID1, rownames(MID1) %in% DEG_PPI)

MID2 <- read.csv("MID2filteredresult.csv")
rownames(MID2) <- MID2$Gene.Symbol
MID2 <- MID2[,39:49]
MID2 <- subset(MID2, rownames(MID2) %in% DEG_PPI)

MOR.SN <- read.csv("MOR.SNfilteredresult.csv")
rownames(MOR.SN) <- MOR.SN$Gene.Symbol
MOR.SN <- MOR.SN[,36:59]
MOR.SN <- subset(MOR.SN, rownames(MOR.SN) %in% DEG_PPI)

DUM <- read.csv("DUM_UniqueGene_DESeq2.csv")
rownames(DUM) <- DUM$hgnc_symbol
DUM <- DUM[,53:81]
DUM <- subset(DUM, rownames(DUM) %in% DEG_PPI)




#Find the gene names that all datasets have in common
DEG_com <- Reduce(intersect, list(rownames(DIJ), rownames(FFR), 
                                  rownames(LEW),rownames(MID1),rownames(MID2),
                                  rownames(MID3),rownames(MID4),rownames(MOR.FC),
                                  rownames(MOR.SN), rownames(DUM)))

#Subset each dataset with these common names so they are all the same size
DIJ <- subset(DIJ, rownames(DIJ) %in% DEG_com)
DUM <- subset(DUM, rownames(DUM) %in% DEG_com)
FFR <- subset(FFR, rownames(FFR) %in% DEG_com)
LEW <- subset(LEW, rownames(LEW) %in% DEG_com)
MID1 <- subset(MID1, rownames(MID1) %in% DEG_com)
MID2 <- subset(MID2, rownames(MID2) %in% DEG_com)
MID3 <- subset(MID3, rownames(MID3) %in% DEG_com)
MID4 <- subset(MID4, rownames(MID4) %in% DEG_com)
MOR.FC <- subset(MOR.FC, rownames(MOR.FC) %in% DEG_com)
MOR.SN <- subset(MOR.SN, rownames(MOR.SN) %in% DEG_com)

setwd("/users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/FamilialBlood")

write.csv(DIJ, "DIJ_DEGfilt.csv")
write.csv(DUM, "DUM_DEGfilt.csv")
write.csv(FFR, "FFR_DEGfilt.csv")
write.csv(LEW, "LEW_DEGfilt.csv")
write.csv(MID1, "MID1_DEGfilt.csv")
write.csv(MID2, "MID2_DEGfilt.csv")
write.csv(MID3, "MID3_DEGfilt.csv")
write.csv(MID4, "MID4_DEGfilt.csv")
write.csv(MOR.FC, "MOR.FC_DEGfilt.csv")
write.csv(MOR.SN, "MOR.SN_DEGfilt.csv")

#Run Correlation analysis on Sharc


#### filter correlations #### BOT/BOT2 not included due to less than 4 samples
setwd("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/FamilialBlood/")

DIJ <- read.csv("DIJcorresult.csv")
DIJ$Gene1 <- as.character(lapply(strsplit(as.character(DIJ$X), "\\:"), "[", 2))
DIJ$Gene2 <- as.character(lapply(strsplit(as.character(DIJ$X), "\\:"), "[", 1))
DIJ <- DIJ[,c(5,6,1,2,3,4)]

DUM <- read.csv("DUMcorresult.csv")
DUM$Gene1 <- as.character(lapply(strsplit(as.character(DUM$X), "\\:"), "[", 2))
DUM$Gene2 <- as.character(lapply(strsplit(as.character(DUM$X), "\\:"), "[", 1))
DUM <- DUM[,c(5,6,1,2,3,4)]
DUM$X <- paste(DUM$Gene1,":",DUM$Gene2, sep = "")

FFR <- read.csv("FFRcorresult.csv")
FFR$Gene1 <- as.character(lapply(strsplit(as.character(FFR$X), "\\:"), "[", 2))
FFR$Gene2 <- as.character(lapply(strsplit(as.character(FFR$X), "\\:"), "[", 1))
FFR <- FFR[,c(5,6,1,2,3,4)]
FFR$X <- paste(FFR$Gene1,":",FFR$Gene2, sep = "")

LEW <- read.csv("LEWcorresult.csv")
LEW$Gene1 <- as.character(lapply(strsplit(as.character(LEW$X), "\\:"), "[", 2))
LEW$Gene2 <- as.character(lapply(strsplit(as.character(LEW$X), "\\:"), "[", 1))
LEW <- LEW[,c(5,6,1,2,3,4)]

MID1 <- read.csv("MID1corresult.csv")
MID1$Gene1 <- as.character(lapply(strsplit(as.character(MID1$X), "\\:"), "[", 2))
MID1$Gene2 <- as.character(lapply(strsplit(as.character(MID1$X), "\\:"), "[", 1))
MID1 <- MID1[,c(5,6,1,2,3,4)]
MID1$X <- paste(MID1$Gene1,":",MID1$Gene2, sep = "")

MID2 <- read.csv("MID2corresult.csv")
MID2$Gene1 <- as.character(lapply(strsplit(as.character(MID2$X), "\\:"), "[", 2))
MID2$Gene2 <- as.character(lapply(strsplit(as.character(MID2$X), "\\:"), "[", 1))
MID2 <- MID2[,c(5,6,1,2,3,4)]

MID3 <- read.csv("MID3corresult.csv")
MID3$Gene1 <- as.character(lapply(strsplit(as.character(MID3$X), "\\:"), "[", 2))
MID3$Gene2 <- as.character(lapply(strsplit(as.character(MID3$X), "\\:"), "[", 1))
MID3 <- MID3[,c(5,6,1,2,3,4)]

MID4 <- read.csv("MID4corresult.csv")
MID4$Gene1 <- as.character(lapply(strsplit(as.character(MID4$X), "\\:"), "[", 2))
MID4$Gene2 <- as.character(lapply(strsplit(as.character(MID4$X), "\\:"), "[", 1))
MID4 <- MID4[,c(5,6,1,2,3,4)]
MID4$X <- paste(MID4$Gene1,":",MID4$Gene2, sep = "")

MOR.FC<- read.csv("MOR.FCcorresult.csv")
MOR.FC$Gene1 <- as.character(lapply(strsplit(as.character(MOR.FC$X), "\\:"), "[", 2))
MOR.FC$Gene2 <- as.character(lapply(strsplit(as.character(MOR.FC$X), "\\:"), "[", 1))
MOR.FC <- MOR.FC[,c(5,6,1,2,3,4)]

MOR.SN<- read.csv("MOR.SNcorresult.csv")
MOR.SN$Gene1 <- as.character(lapply(strsplit(as.character(MOR.SN$X), "\\:"), "[", 2))
MOR.SN$Gene2 <- as.character(lapply(strsplit(as.character(MOR.SN$X), "\\:"), "[", 1))
MOR.SN <- MOR.SN[,c(5,6,1,2,3,4)]

thresh <- 0.1
### Filter by r value
DIJ_cor.5 <- DIJ[DIJ$reg.mat > thresh | DIJ$reg.mat < -thresh,]
# DUM_cor.5 <- DUM[DUM$reg.mat > thresh | DUM$reg.mat < -thresh,]
FFR_cor.5 <- FFR[FFR$reg.mat > thresh | FFR$reg.mat < -thresh,]
LEW_cor.5 <- LEW[LEW$reg.mat > thresh | LEW$reg.mat < -thresh,]
MID1_cor.5 <- MID1[MID1$reg.mat > thresh | MID1$reg.mat < -thresh,]
MID2_cor.5 <- MID2[MID2$reg.mat > thresh | MID2$reg.mat < -thresh,]
MID3_cor.5 <- MID3[MID3$reg.mat > thresh | MID3$reg.mat < -thresh,]
MID4_cor.5 <- MID4[MID4$reg.mat > thresh | MID4$reg.mat < -thresh,]
# MOR.FC_cor.5 <- MOR.FC[MOR.FC$reg.mat > thresh | MOR.FC$reg.mat < -thresh,]
MOR.SN_cor.5 <- MOR.SN[MOR.SN$reg.mat > thresh | MOR.SN$reg.mat < -thresh,]


### Find matches 

Commonedge <- Reduce(intersect, list(DIJ_cor.5$X,FFR_cor.5$X,
                                     LEW_cor.5$X, MID1_cor.5$X, MID2_cor.5$X,
                                     MID3_cor.5$X, MID4_cor.5$X,
                                     MOR.SN_cor.5$X))


#Subset each dataset with these common names so they are all the same size
DIJ_CE <- subset(DIJ_cor.5, DIJ_cor.5$X %in% Commonedge)
DIJ_CE <- DIJ_CE[order(DIJ_CE$X),]
# DUM_CE <- subset(DUM_cor.5, DUM_cor.5$X %in% Commonedge)
# DUM_CE <- DUM_CE[order(DUM_CE$X),]
FFR_CE <- subset(FFR_cor.5, FFR_cor.5$X %in% Commonedge)
FFR_CE <- FFR_CE[order(FFR_CE$X),]
LEW_CE <- subset(LEW_cor.5, LEW_cor.5$X %in% Commonedge)
LEW_CE <- LEW_CE[order(LEW_CE$X),]
MID1_CE <- subset(MID1_cor.5, MID1_cor.5$X %in% Commonedge)
MID1_CE <- MID1_CE[order(MID1_CE$X),]
MID2_CE <- subset(MID2_cor.5, MID2_cor.5$X %in% Commonedge)
MID2_CE <- MID2_CE[order(MID2_CE$X),]
MID3_CE <- subset(MID3_cor.5, MID3_cor.5$X %in% Commonedge)
MID3_CE <- MID3_CE[order(MID3_CE$X),]
MID4_CE <- subset(MID4_cor.5, MID4_cor.5$X %in% Commonedge)
MID4_CE <- MID4_CE[order(MID4_CE$X),]
# MOR.FC_CE <- subset(MOR.FC_cor.5, MOR.FC_cor.5$X %in% Commonedge)
# MOR.FC_CE <- MOR.FC_CE[order(MOR.FC_CE$X),]
MOR.SN_CE <- subset(MOR.SN_cor.5, MOR.SN_cor.5$X %in% Commonedge)
MOR.SN_CE <- MOR.SN_CE[order(MOR.SN_CE$X),]



CommonGroup <- data.frame(row.names = DIJ_CE$X,
                          DIJ = DIJ_CE$reg.mat,
                          FFR = FFR_CE$reg.mat,
                          LEW = LEW_CE$reg.mat,
                          MID1 = MID1_CE$reg.mat,
                          MID2 = MID2_CE$reg.mat, 
                          MID3 = MID3_CE$reg.mat, 
                          MID4 = MID4_CE$reg.mat, 
                          MOR.SN = MOR.SN_CE$reg.mat)


CG_conserved_up <- CommonGroup[apply(CommonGroup, MARGIN = 1, function(x) all(x > 0)), ]
CG_conserved_down <- CommonGroup[apply(CommonGroup, MARGIN = 1, function(x) all(x < 0)), ]

setwd("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/FamilialBlood/")
CG_samedir <- rbind(CG_conserved_up, CG_conserved_down)
CG_samedir$corMean <- rowMeans(CG_samedir, na.rm = FALSE, dims = 1)
CG_samedir$Gene <- rownames(CG_samedir)
CG_samedir$Gene1 <- as.character(lapply(strsplit(as.character(CG_samedir$Gene), "\\:"), "[", 2))
CG_samedir$Gene2 <- as.character(lapply(strsplit(as.character(CG_samedir$Gene), "\\:"), "[", 1))

write.csv(CG_samedir, "PD_samedir_1.csv", quote = F, row.names = F)

nodetable <- read.csv("PD_samedir_1node.csv")
nuggenes <- as.character(nodetable$name)
PDgenes <- readLines("/Users/clairegreen/Documents/PhD/Parkinsons/ParkinsonsDiseaseMalacards.txt")
celltype <- read.csv("~/Documents/PhD/TDP-43/TDP-43_Code/Results/PPI_Network/Zhang_BrainCelltype_Markers_braingenes.csv")
DEG <- readLines("ALS_sfblood_ALLgenes.txt")
nalls <- readLines("/Users/clairegreen/Documents/PhD/Parkinsons/NallsPDGWAS.txt")
targetvalPD <- c("SIRT2", "DNM1L", "STMN1", "DNAJA3", "TBK1", "RTCA", "ANXA7", "DNAJC12", "RTN4", "ADSL", "MDH1","ATP6V1G2", 
                 "YWHAZ", "ETS1")

nodetable$PDMalacards <- nodetable$shared.name %in% PDgenes
nodetable$DEG <- nodetable$name %in% DEG
nodetable$targetvalPD <- nodetable$shared.name %in% targetvalPD
nodetable$targetvalPD <- nodetable$shared.name %in% nalls

nodetable_celltype <- merge(celltype, nodetable, by.x = "Gene.symbol",  by.y = "shared.name", all = T)
nodetable_celltype <- subset(nodetable_celltype, !(nodetable_celltype$SUID == "NA"))


nodetable_celltype$targetvalPD <- nodetable_celltype$Gene.symbol %in% targetvalPD
write.csv(nodetable_celltype, "ModifiedPDNodetable.csv", row.names = F)

This analysis didn’t go as well as the TDP-43 case. The correlations were much more variable meaning I had to reduce the thresholds from 0.5 to 0.1. Additionally, I removed four datasets - the LRRK2 datasets couldn’t be used because they only had 2 and 3 samples - correlation would never calculated properly. DUM was an RNA-seq dataset so I was losing annotation coverage, and MOR.FC showed a really odd rho value distribution…

Looking back this is probably because it only has 3 control samples.

Using these 8 datasets, with a threshold of 0.1, This generated a network containing 279 nodes and 292 edges. Within this network was one clear largest connected component of 208 nodes and 249 edges.

If I increase the threshold to 0.2, the resulting network is much smaller:

Functional Enrichment

http://amp.pharm.mssm.edu/Enrichr/enrich?dataset=3zurx

The enrichments suggest that there is an immune process signal. There is also still an enrichment in the mitochondria for GO Cellular Component.

Rerunning after removal of LEW samples

I realised after looking more closely at the paper that the LEW samples had some olivary nucleus samples which were supposed to be unaffected controls. Removing these samples meant I needed to essentially run everything again (again).

Common DEGs

Rerunning left 210 genes this time (ALS_sfblood_ALLgenes.txt).

> intersect(resultsup, PDgenes)
[1] "MSX1" "SPR" 
> intersect(resultsdown, PDgenes)
[1] "SNCA"    "ALDH1A1" "UCHL1"   "CYCS"  

Functional enrichment in EnrichR for upregulated and downregulated genes. Upregulated signal isn’t entirely clear; a number of signalling and DNA-binding pathways are enriched but not with the strongest p values. Alternatively there is quite a strong signal in the downregulated genes. A number of neurodegenerative pathways (inc Parkinson’s disease) are enriched, as well as the electron transport chain/mitochondrial processes (mitochondrion (GO:0005739) padj = 4.711e-8, 23 genes). GO Biological Process also implicates autophagy, which highlights the presence of CHMP2B - a causative ALS/FTLD gene.

Symbol	Description	Category	GIFTs	Matched Phenotypes	Matched Phenotypes Scount	Global Rank (Total Genes 11967)	Score
SNCA	Synuclein Alpha	Protein Coding	61	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	2	378.27
UCHL1	Ubiquitin C-Terminal Hydrolase L1	Protein Coding	62	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	7	209.64
CYCS	Cytochrome C, Somatic	Protein Coding	57	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	63	63.23
NDUFV2	NADH:Ubiquinone Oxidoreductase Core Subunit V2	Protein Coding	53	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	106	51.16
ALDH1A1	Aldehyde Dehydrogenase 1 Family Member A1	Protein Coding	57	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	153	42.23
SPR	Sepiapterin Reductase	Protein Coding	58	Parkinson’s Disease, LRRK2, late-onset	3	126	40.38
MSX1	Msh Homeobox 1	Protein Coding	55	Parkinson’s Disease, LRRK2, late-onset	3	169	33.61
NDUFS1	NADH:Ubiquinone Oxidoreductase Core Subunit S1	Protein Coding	56	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	213	28.87
NDUFS3	NADH:Ubiquinone Oxidoreductase Core Subunit S3	Protein Coding	56	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	236	26.54
SYT11	Synaptotagmin 11	Protein Coding	50	Parkinson’s Disease, LRRK2, neurodegeneration	3	239	22.93
CASP7	Caspase 7	Protein Coding	59	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	309	21.45
TOMM20	Translocase Of Outer Mitochondrial Membrane 20	Protein Coding	48	Parkinson’s Disease, LRRK2, neurodegeneration	3	362	16.56
MFN2	Mitofusin 2	Protein Coding	59	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	424	16.55
IGF1R	Insulin Like Growth Factor 1 Receptor	Protein Coding	66	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	435	16.27
MAP1B	Microtubule Associated Protein 1B	Protein Coding	51	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	474	15.18
TMEM126B	Transmembrane Protein 126B	Protein Coding	47	Parkinson’s Disease, LRRK2	2	363	13.45
PTS	6-Pyruvoyltetrahydropterin Synthase	Protein Coding	57	Parkinson’s Disease, late-onset, neurodegeneration	3	556	11.88
CASP6	Caspase 6	Protein Coding	57	Parkinson’s Disease, LRRK2, neurodegeneration	3	573	11.74
RAB7A	RAB7A, Member RAS Oncogene Family	Protein Coding	58	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	759	11.00
PPP2R2B	Protein Phosphatase 2 Regulatory Subunit Bbeta	Protein Coding	55	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	788	10.69
PYROXD1	Pyridine Nucleotide-Disulphide Oxidoreductase Domain 1	Protein Coding	44	Parkinson’s Disease, late-onset	2	487	10.60
AGRP	Agouti Related Neuropeptide	Protein Coding	51	late-onset, neurodegeneration	2	504	10.27
AIFM1	Apoptosis Inducing Factor Mitochondria Associated 1	Protein Coding	59	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	898	9.70
FLT1	Fms Related Tyrosine Kinase 1	Protein Coding	62	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	952	9.26
RTN4	Reticulon 4	Protein Coding	54	LRRK2, late-onset, neurodegeneration	3	792	9.21
DYNC1H1	Dynein Cytoplasmic 1 Heavy Chain 1	Protein Coding	54	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	965	9.11
TUBA4A	Tubulin Alpha 4a	Protein Coding	56	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	968	9.09
CHMP2B	Charged Multivesicular Body Protein 2B	Protein Coding	53	Parkinson’s Disease, late-onset, neurodegeneration	3	894	8.43
RGS2	Regulator Of G Protein Signaling 2	Protein Coding	51	Parkinson’s Disease, LRRK2, neurodegeneration	3	944	8.07
HIST1H1C	Histone Cluster 1 H1 Family Member C	Protein Coding	48	Parkinson’s Disease	1	520	7.11
PAWR	Pro-Apoptotic WT1 Regulator	Protein Coding	48	Parkinson’s Disease, neurodegeneration	2	982	6.36
MS4A6A	Membrane Spanning 4-Domains A6A	Protein Coding	45	Parkinson’s Disease, late-onset, neurodegeneration	3	1259	6.26
MYO1C	Myosin IC	Protein Coding	53	Parkinson’s Disease, LRRK2, neurodegeneration	3	1362	5.78
SUMO1	Small Ubiquitin-Like Modifier 1	Protein Coding	56	Parkinson’s Disease, neurodegeneration	2	1277	5.04
ACLY	ATP Citrate Lyase	Protein Coding	53	Parkinson’s Disease, LRRK2, neurodegeneration	3	1577	4.91
SDHA	Succinate Dehydrogenase Complex Flavoprotein Subunit A	Protein Coding	56	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	1850	4.77
PGF	Placental Growth Factor	Protein Coding	54	LRRK2, late-onset	2	1361	4.72
TRADD	TNFRSF1A Associated Via Death Domain	Protein Coding	51	Parkinson’s Disease, LRRK2, neurodegeneration	3	1694	4.57
HSPA1L	Heat Shock Protein Family A (Hsp70) Member 1 Like	Protein Coding	52	Parkinson’s Disease, LRRK2	2	1457	4.38
GABBR2	Gamma-Aminobutyric Acid Type B Receptor Subunit 2	Protein Coding	57	Parkinson’s Disease, LRRK2, late-onset	3	1880	4.04
PGAM1	Phosphoglycerate Mutase 1	Protein Coding	54	Parkinson’s Disease, neurodegeneration	2	1602	3.95
ATP6V1E1	ATPase H+ Transporting V1 Subunit E1	Protein Coding	52	Parkinson’s Disease, LRRK2, late-onset	3	2008	3.70
FZD5	Frizzled Class Receptor 5	Protein Coding	57	Parkinson’s Disease, LRRK2	2	1718	3.67
MRPS22	Mitochondrial Ribosomal Protein S22	Protein Coding	50	Parkinson’s Disease, neurodegeneration	2	1776	3.54
PLIN2	Perilipin 2	Protein Coding	51	late-onset, neurodegeneration	2	1805	3.47
CIRBP	Cold Inducible RNA Binding Protein	Protein Coding	48	Parkinson’s Disease, LRRK2, neurodegeneration	3	2119	3.38
SNX10	Sorting Nexin 10	Protein Coding	49	Parkinson’s Disease, LRRK2	2	1913	3.22
FABP3	Fatty Acid Binding Protein 3	Protein Coding	54	Parkinson’s Disease, neurodegeneration	2	1947	3.13
BCAT2	Branched Chain Amino Acid Transaminase 2	Protein Coding	53	Parkinson’s Disease, neurodegeneration	2	1987	3.05
ADH1B	Alcohol Dehydrogenase 1B (Class I), Beta Polypeptide	Protein Coding	53	Parkinson’s Disease, late-onset	2	2004	3.03
RARRES2	Retinoic Acid Receptor Responder 2	Protein Coding	50	late-onset	1	1536	2.92
DNAJC24	DnaJ Heat Shock Protein Family (Hsp40) Member C24	Protein Coding	44	LRRK2, late-onset	2	2118	2.76
PRPF4	Pre-MRNA Processing Factor 4	Protein Coding	51	Parkinson’s Disease, LRRK2, neurodegeneration	3	2366	2.55
FLCN	Folliculin	Protein Coding	50	Parkinson’s Disease, late-onset	2	2227	2.46
COPS5	COP9 Signalosome Subunit 5	Protein Coding	51	Parkinson’s Disease, LRRK2, neurodegeneration	3	2450	2.31
ENOPH1	Enolase-Phosphatase 1	Protein Coding	49	Parkinson’s Disease	1	2186	1.83
ZC3HAV1	Zinc Finger CCCH-Type Containing, Antiviral 1	Protein Coding	46	Parkinson’s Disease, neurodegeneration	2	2481	1.81
PLCG1	Phospholipase C Gamma 1	Protein Coding	56	Parkinson’s Disease, LRRK2	2	2492	1.80
STAB1	Stabilin 1	Protein Coding	48	Parkinson’s Disease	1	2218	1.76
HP1BP3	Heterochromatin Protein 1 Binding Protein 3	Protein Coding	44	Parkinson’s Disease, neurodegeneration	2	2528	1.71
MLLT11	MLLT11, Transcription Factor 7 Cofactor	Protein Coding	45	late-onset	1	2261	1.68
AGL	Amylo-Alpha-1, 6-Glucosidase, 4-Alpha-Glucanotransferase	Protein Coding	55	late-onset	1	2261	1.68
MAVS	Mitochondrial Antiviral Signaling Protein	Protein Coding	50	late-onset	1	2261	1.68
TAZ	Tafazzin	Protein Coding	52	late-onset	1	2261	1.68
SLC6A1	Solute Carrier Family 6 Member 1	Protein Coding	57	Parkinson’s Disease, neurodegeneration	2	2545	1.67
STAT4	Signal Transducer And Activator Of Transcription 4	Protein Coding	56	LRRK2, late-onset	2	2559	1.63
EIF4E	Eukaryotic Translation Initiation Factor 4E	Protein Coding	59	Parkinson’s Disease, LRRK2	2	2565	1.62
NIPSNAP1	Nipsnap Homolog 1	Protein Coding	49	Parkinson’s Disease, LRRK2	2	2565	1.62
RAD51C	RAD51 Paralog C	Protein Coding	51	Parkinson’s Disease, LRRK2	2	2573	1.60
NCALD	Neurocalcin Delta	Protein Coding	51	Parkinson’s Disease, LRRK2	2	2573	1.60
LRRC1	Leucine Rich Repeat Containing 1	Protein Coding	44	Parkinson’s Disease, LRRK2	2	2573	1.60
PKNOX1	PBX/Knotted 1 Homeobox 1	Protein Coding	47	Parkinson’s Disease, LRRK2	2	2573	1.60
GABARAPL1	GABA Type A Receptor Associated Protein Like 1	Protein Coding	53	Parkinson’s Disease, neurodegeneration	2	2609	1.51
MAP1LC3B	Microtubule Associated Protein 1 Light Chain 3 Beta	Protein Coding	52	Parkinson’s Disease, neurodegeneration	2	2611	1.50
LYVE1	Lymphatic Vessel Endothelial Hyaluronan Receptor 1	Protein Coding	51	late-onset, neurodegeneration	2	2651	1.41
DYNC1I1	Dynein Cytoplasmic 1 Intermediate Chain 1	Protein Coding	48	Parkinson’s Disease, neurodegeneration	2	2666	1.37
FOXO4	Forkhead Box O4	Protein Coding	51	Parkinson’s Disease, LRRK2	2	2667	1.37
NME3	NME/NM23 Nucleoside Diphosphate Kinase 3	Protein Coding	51	Parkinson’s Disease, neurodegeneration	2	2680	1.33
SERTAD3	SERTA Domain Containing 3	Protein Coding	38	neurodegeneration	1	2476	1.29
USP8	Ubiquitin Specific Peptidase 8	Protein Coding	57	Parkinson’s Disease, LRRK2, neurodegeneration	3	2776	1.27
ATG14	Autophagy Related 14	Protein Coding	43	Parkinson’s Disease	1	2551	1.17
LDLRAD4	Low Density Lipoprotein Receptor Class A Domain Containing 4	Protein Coding	40	Parkinson’s Disease	1	2551	1.17
TAF4	TATA-Box Binding Protein Associated Factor 4	Protein Coding	48	LRRK2, neurodegeneration	2	2778	1.03
RBL1	RB Transcriptional Corepressor Like 1	Protein Coding	51	LRRK2, late-onset	2	2802	0.95
IARS2	Isoleucyl-TRNA Synthetase 2, Mitochondrial	Protein Coding	49	Parkinson’s Disease	1	2681	0.94
KLHL36	Kelch Like Family Member 36	Protein Coding	41	late-onset	1	2683	0.93
ST3GAL5	ST3 Beta-Galactoside Alpha-2,3-Sialyltransferase 5	Protein Coding	55	late-onset	1	2706	0.88
CLIC2	Chloride Intracellular Channel 2	Protein Coding	49	late-onset	1	2706	0.88
DDX42	DEAD-Box Helicase 42	Protein Coding	46	Parkinson’s Disease	1	2711	0.87
ANXA6	Annexin A6	Protein Coding	53	Parkinson’s Disease	1	2711	0.87
CCDC40	Coiled-Coil Domain Containing 40	Protein Coding	45	Parkinson’s Disease	1	2711	0.87
LAG3	Lymphocyte Activating 3	Protein Coding	44	Parkinson’s Disease	1	2711	0.87
SDCCAG3	Serologically Defined Colon Cancer Antigen 3	Protein Coding	41	Parkinson’s Disease	1	2711	0.87
BCAR3	BCAR3, NSP Family Adaptor Protein	Protein Coding	49	Parkinson’s Disease	1	2711	0.87
IMP4	IMP4, U3 Small Nucleolar Ribonucleoprotein	Protein Coding	42	Parkinson’s Disease	1	2711	0.87
LETMD1	LETM1 Domain Containing 1	Protein Coding	43	Parkinson’s Disease	1	2711	0.87
PTP4A1	Protein Tyrosine Phosphatase Type IVA, Member 1	Protein Coding	51	Parkinson’s Disease	1	2711	0.87
RCC1	Regulator Of Chromosome Condensation 1	Protein Coding	49	LRRK2, neurodegeneration	2	2829	0.86
TXNL1	Thioredoxin Like 1	Protein Coding	47	late-onset	1	2741	0.79
CD84	CD84 Molecule	Protein Coding	51	late-onset	1	2806	0.66
DUSP9	Dual Specificity Phosphatase 9	Protein Coding	50	LRRK2	1	2807	0.66
FHIT	Fragile Histidine Triad	Protein Coding	54	Parkinson’s Disease, late-onset	2	2896	0.61
ANGPT2	Angiopoietin 2	Protein Coding	55	LRRK2, late-onset	2	2901	0.58
INHBB	Inhibin Beta B Subunit	Protein Coding	53	LRRK2, late-onset	2	2901	0.58
CALML4	Calmodulin Like 4	Protein Coding	41	LRRK2	1	2853	0.54
HDAC7	Histone Deacetylase 7	Protein Coding	52	LRRK2	1	2853	0.54
ATP2B2	ATPase Plasma Membrane Ca2+ Transporting 2	Protein Coding	54	LRRK2, neurodegeneration	2	2915	0.52
PPP2R1B	Protein Phosphatase 2 Scaffold Subunit Abeta	Protein Coding	57	LRRK2	1	2905	0.39
RGS4	Regulator Of G Protein Signaling 4	Protein Coding	52	LRRK2	1	2905	0.39
KPNA1	Karyopherin Subunit Alpha 1	Protein Coding	51	Parkinson’s Disease, neurodegeneration	2	2942	0.35
NRXN3	Neurexin 3	Protein Coding	51	neurodegeneration	1	2920	0.34
SARS	Seryl-TRNA Synthetase	Protein Coding	53	neurodegeneration	1	2920	0.34
PGRMC1	Progesterone Receptor Membrane Component 1	Protein Coding	53	neurodegeneration	1	2920	0.34
RDH16	Retinol Dehydrogenase 16 (All-Trans)	Protein Coding	44	LRRK2	1	2936	0.28
SMAD6	SMAD Family Member 6	Protein Coding	54	LRRK2	1	2936	0.28
NFATC4	Nuclear Factor Of Activated T Cells 4	Protein Coding	53	LRRK2	1	2936	0.28
REEP5	Receptor Accessory Protein 5	Protein Coding	46	LRRK2	1	2936	0.28
EFNA4	Ephrin A4	Protein Coding	50	LRRK2	1	2936	0.28
MST1	Macrophage Stimulating 1	Protein Coding	55	LRRK2	1	2936	0.28
FSTL3	Follistatin Like 3	Protein Coding	48	LRRK2	1	2936	0.28
LAMA5	Laminin Subunit Alpha 5	Protein Coding	51	LRRK2	1	2936	0.28
PRC1	Protein Regulator Of Cytokinesis 1	Protein Coding	47	LRRK2	1	2936	0.28
LAMB2	Laminin Subunit Beta 2	Protein Coding	55	LRRK2	1	2936	0.28
ZNF629	Zinc Finger Protein 629	Protein Coding	38	LRRK2	1	2938	0.27
NR6A1	Nuclear Receptor Subfamily 6 Group A Member 1	Protein Coding	50	LRRK2	1	2938	0.27
ATP8A2	ATPase Phospholipid Transporting 8A2	Protein Coding	50	LRRK2	1	2938	0.27
AREL1	Apoptosis Resistant E3 Ubiquitin Protein Ligase 1	Protein Coding	38	LRRK2	1	2938	0.27
ZNF692	Zinc Finger Protein 692	Protein Coding	40	LRRK2	1	2938	0.27
ROBO3	Roundabout Guidance Receptor 3	Protein Coding	52	LRRK2	1	2938	0.27
ACAT2	Acetyl-CoA Acetyltransferase 2	Protein Coding	55	LRRK2	1	2938	0.27
STX2	Syntaxin 2	Protein Coding	48	LRRK2	1	2938	0.27
TCF3	Transcription Factor 3	Protein Coding	55	LRRK2	1	2938	0.27
RAB6B	RAB6B, Member RAS Oncogene Family	Protein Coding	46	LRRK2	1	2938	0.27
DOCK6	Dedicator Of Cytokinesis 6	Protein Coding	50	LRRK2	1	2938	0.27
INSIG2	Insulin Induced Gene 2	Protein Coding	47	LRRK2	1	2938	0.27
DNAJB9	DnaJ Heat Shock Protein Family (Hsp40) Member B9	Protein Coding	45	LRRK2	1	2938	0.27
HLTF	Helicase Like Transcription Factor	Protein Coding	48	LRRK2	1	2938	0.27
VPS4B	Vacuolar Protein Sorting 4 Homolog B	Protein Coding	51	Parkinson’s Disease	1	2952	0.18
SLC23A2	Solute Carrier Family 23 Member 2	Protein Coding	52	Parkinson’s Disease	1	2952	0.18
TULP2	Tubby Like Protein 2	Protein Coding	44	late-onset	1	2955	0.13
ALAS1	5’-Aminolevulinate Synthase 1	Protein Coding	53	late-onset	1	2955	0.13
PTBP2	Polypyrimidine Tract Binding Protein 2	Protein Coding	47	neurodegeneration	1	2959	0.07
ZMYND8	Zinc Finger MYND-Type Containing 8	Protein Coding	46	neurodegeneration	1	2959	0.07
WDYHV1	WDYHV Motif Containing 1	Protein Coding	39	neurodegeneration	1	2959	0.07
FAM193B	Family With Sequence Similarity 193 Member B	Protein Coding	42	neurodegeneration	1	2959	0.07
SEZ6L2	Seizure Related 6 Homolog Like 2	Protein Coding	46	neurodegeneration	1	2959	0.07

Protein Interaction Network

Using the 210 DEGs I seeded a network of 2429 nodes and 4097 edges.

library(biomaRt)

setwd("/Users/clairegreen/Documents/PhD/TDP-43/TDP-43_Code/Results/PPI_Network/")

PPI <- read.table("iref14_Human_UP_noDup_table_nodash.txt", header = T)
braingenes <- read.csv("Zhang_BrainCelltype_Markers_braingenes.csv", header = T)

DEG_list <- readLines("~/Documents/PhD/Parkinsons/Parkinsons_Code/Results/FamilialBlood/ALS_sfblood_ALLgenes.txt")

mart <- useMart("ENSEMBL_MART_ENSEMBL",dataset="hsapiens_gene_ensembl", host="www.ensembl.org")
attributes <- listAttributes(mart)
mart_back <- getBM(attributes =c("hgnc_symbol", "uniprotswissprot"), filters="hgnc_symbol", values=DEG_list,  mart=mart)
genelist_Uniprot <- subset(mart_back, !(mart_back$uniprotswissprot == ""))
setwd("/Users/clairegreen/Documents/PhD/Parkinsons/Parkinsons_Code/Results/FamilialBlood/")
write.csv(genelist_Uniprot, "martback.csv", row.names = F)

# IDENTIFY MISSING GENES AND FIND UNIPROT CODES FOR THEM. NB SOME GENES MAY NOT BE PROTEIN CODING #

mart_table <- read.csv("martback.csv", header = T) #A table with the uniprot codes for the DEGs
uniprot_gene <- mart_table$uniprotswissprot

DEG_PPI <- subset(PPI, PPI$V1 %in% uniprot_gene | PPI$V2 %in% uniprot_gene)
rownames(DEG_PPI) <- 1:nrow(DEG_PPI)

write.csv(DEG_PPI, "DEG_PPI_famBlood.csv", row.names = F)

## Convert Uniprot ID to HGNC symbol. Biomart jumbles output so
## Go To https://biodbnet-abcc.ncifcrf.gov/db/db2db.php submit each column of names. Select Uniprot Accession for input and Gene Symbol for output.
# Select "NO" for remove duplicate input valies

DEG_PPI <- read.csv("DEG_PPI_famBlood.csv")

DEG_PPI <- subset(DEG_PPI, DEG_PPI$Gene1 !="-")
DEG_PPI <- subset(DEG_PPI, DEG_PPI$Gene2 !="-")

write.csv(DEG_PPI, "FinalPDPPI.csv", row.names = F, quote = F)

PPI Network

Common Coexpression

I ran common coexpression again using Sharc. DUM was again removed because it had ~100,000 fewer edges than the other datasets. I then plotted the distributions of rho values to check they were normally distributed:

From this it is clear that the LEW and MOR.FC datasets do not have the power for a normal coexpression distribution. These two datasets were therefore removed from the common coexpression edge analysis.

When looking for a common edge, I tried three different minimum Rho values; 0.5, 0.4, and 0.3.

Threshold of 0.5

Threshold of 0.4

Threshold of 0.3

The largest connected component consists of 82 genes and 95 edges.

Largest Connected Component of 0.3

IrefIndex 14.0 PPI between PD Malacards and LCC

Here we can see that there are 25 LCC proteins which forming 45 PPI relationships with 25 PD Malacard genes (1 overlap of PARK7)

VarElect

Although already noted, the VarElect results clearly show the membership of PARK7 in the network. The interesting thing about PARK7 being here is that it is more commonly associated with early-onset or juvenile Parkinson’s disease. Another interesting point is that, although PARK7 PD is early-onset, it’s classified by UniProt as having “slow progression and initial good response to levodopa”.

CYCS or Cytochrome C, Somatic, is a small heme protein that functions as a central component of the electron transport chain in mitochondria. It is closely related to Parkinson’s disease in the literature due to its apparent malfuction in the disease.

Direct VarElect Results

Symbol	Description	Category	GIFTs	Matched Phenotypes	Matched Phenotypes Scount	Global Rank (Total Genes 11967)	P-Value	Score	Average Disease Causing Likelihood
PARK7	Parkinsonism Associated Deglycase	Protein Coding	56	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	5		270.87
CYCS	Cytochrome C, Somatic	Protein Coding	57	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	63		63.23
UBE2E3	Ubiquitin Conjugating Enzyme E2 E3	Protein Coding	51	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	119		48.51
STXBP1	Syntaxin Binding Protein 1	Protein Coding	57	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	370		18.76
VDR	Vitamin D Receptor	Protein Coding	61	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	380		18.20
COX5A	Cytochrome C Oxidase Subunit 5A	Protein Coding	53	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	536		13.99
PGK1	Phosphoglycerate Kinase 1	Protein Coding	59	Parkinson’s Disease, late-onset, neurodegeneration	3	513		12.40
NDUFA8	NADH:Ubiquinone Oxidoreductase Subunit A8	Protein Coding	50	Parkinson’s Disease, LRRK2, late-onset	3	641		10.77
EIF4EBP1	Eukaryotic Translation Initiation Factor 4E Binding Protein 1	Protein Coding	57	Parkinson’s Disease, LRRK2, late-onset	3	715		9.90
DLD	Dihydrolipoamide Dehydrogenase	Protein Coding	61	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	899		9.68
PSMC6	Proteasome 26S Subunit, ATPase 6	Protein Coding	50	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	925		9.51
PLA2G2A	Phospholipase A2 Group IIA	Protein Coding	56	Parkinson’s Disease, late-onset, neurodegeneration	3	770		9.43
RTN4	Reticulon 4	Protein Coding	54	LRRK2, late-onset, neurodegeneration	3	792		9.21
NDUFA5	NADH:Ubiquinone Oxidoreductase Subunit A5	Protein Coding	51	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	998		8.87
UQCRC2	Ubiquinol-Cytochrome C Reductase Core Protein 2	Protein Coding	55	Parkinson’s Disease, LRRK2, neurodegeneration	3	1033		7.46
RAB11A	RAB11A, Member RAS Oncogene Family	Protein Coding	55	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	1321		6.93
SFXN1	Sideroflexin 1	Protein Coding	47	Parkinson’s Disease, LRRK2, neurodegeneration	3	1241		6.34
RAB1A	RAB1A, Member RAS Oncogene Family	Protein Coding	51	Parkinson’s Disease, LRRK2, neurodegeneration	3	1282		6.14
PSMC5	Proteasome 26S Subunit, ATPase 5	Protein Coding	51	Parkinson’s Disease, LRRK2, neurodegeneration	3	1311		6.03
PSMA7	Proteasome Subunit Alpha 7	Protein Coding	55	Parkinson’s Disease, LRRK2, neurodegeneration	3	1482		5.27
SUMO1	Small Ubiquitin-Like Modifier 1	Protein Coding	56	Parkinson’s Disease, neurodegeneration	2	1277		5.04
ACLY	ATP Citrate Lyase	Protein Coding	53	Parkinson’s Disease, LRRK2, neurodegeneration	3	1577		4.91
SDHA	Succinate Dehydrogenase Complex Flavoprotein Subunit A	Protein Coding	56	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	1850		4.77
PSMD6	Proteasome 26S Subunit, Non-ATPase 6	Protein Coding	47	Parkinson’s Disease, LRRK2, neurodegeneration	3	1696		4.56
UQCRC1	Ubiquinol-Cytochrome C Reductase Core Protein 1	Protein Coding	50	Parkinson’s Disease, LRRK2, neurodegeneration	3	1746		4.43
PDHB	Pyruvate Dehydrogenase E1 Beta Subunit	Protein Coding	56	Parkinson’s Disease, LRRK2, neurodegeneration	3	1781		4.32
NR4A1	Nuclear Receptor Subfamily 4 Group A Member 1	Protein Coding	55	Parkinson’s Disease, LRRK2, neurodegeneration	3	1865		4.09
IARS	Isoleucyl-TRNA Synthetase	Protein Coding	55	Parkinson’s Disease, neurodegeneration	2	1597		3.97
ATP6V1E1	ATPase H+ Transporting V1 Subunit E1	Protein Coding	52	Parkinson’s Disease, LRRK2, late-onset	3	2008		3.70
MAP2K1	Mitogen-Activated Protein Kinase Kinase 1	Protein Coding	65	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	2197		3.60
PPP2CA	Protein Phosphatase 2 Catalytic Subunit Alpha	Protein Coding	56	Parkinson’s Disease, LRRK2, neurodegeneration	3	2050		3.58
TRIM37	Tripartite Motif Containing 37	Protein Coding	51	neurodegeneration	1	1309		3.49
UQCRFS1	Ubiquinol-Cytochrome C Reductase, Rieske Iron-Sulfur Polypeptide 1	Protein Coding	51	Parkinson’s Disease, LRRK2, neurodegeneration	3	2137		3.31
VAV1	Vav Guanine Nucleotide Exchange Factor 1	Protein Coding	57	LRRK2, late-onset	2	1938		3.15
RUNX2	Runt Related Transcription Factor 2	Protein Coding	57	late-onset	1	1481		3.04
CUL3	Cullin 3	Protein Coding	55	Parkinson’s Disease, LRRK2, neurodegeneration	3	2349		2.62
PPIA	Peptidylprolyl Isomerase A	Protein Coding	57	Parkinson’s Disease, LRRK2, neurodegeneration	3	2366		2.55
UBE2K	Ubiquitin Conjugating Enzyme E2 K	Protein Coding	51	Parkinson’s Disease, LRRK2, neurodegeneration	3	2388		2.49
COPS5	COP9 Signalosome Subunit 5	Protein Coding	51	Parkinson’s Disease, LRRK2, neurodegeneration	3	2450		2.31
PDHA1	Pyruvate Dehydrogenase E1 Alpha 1 Subunit	Protein Coding	59	Parkinson’s Disease, LRRK2, neurodegeneration	3	2547		2.04
DDX1	DEAD-Box Helicase 1	Protein Coding	51	Parkinson’s Disease, neurodegeneration	2	2528		1.71
ANXA7	Annexin A7	Protein Coding	51	late-onset, neurodegeneration	2	2571		1.60
YARS	Tyrosyl-TRNA Synthetase	Protein Coding	53	late-onset, neurodegeneration	2	2576		1.60
PSMD12	Proteasome 26S Subunit, Non-ATPase 12	Protein Coding	49	Parkinson’s Disease, LRRK2, neurodegeneration	3	2710		1.50
EPS15	Epidermal Growth Factor Receptor Pathway Substrate 15	Protein Coding	54	Parkinson’s Disease, LRRK2	2	2701		1.26
PIK3CB	Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Beta	Protein Coding	57	LRRK2, neurodegeneration	2	2746		1.11
MDH1	Malate Dehydrogenase 1	Protein Coding	55	Parkinson’s Disease, neurodegeneration	2	2770		1.04
ATP6V1B2	ATPase H+ Transporting V1 Subunit B2	Protein Coding	56	LRRK2, neurodegeneration	2	2775		1.03
GABARAPL2	GABA Type A Receptor Associated Protein Like 2	Protein Coding	55	Parkinson’s Disease	1	2652		0.99
IARS2	Isoleucyl-TRNA Synthetase 2, Mitochondrial	Protein Coding	49	Parkinson’s Disease	1	2681		0.94
STRAP	Serine/Threonine Kinase Receptor Associated Protein	Protein Coding	45	LRRK2, neurodegeneration	2	2823		0.88
FOSL1	FOS Like 1, AP-1 Transcription Factor Subunit	Protein Coding	52	LRRK2, neurodegeneration	2	2826		0.87
ICK	Intestinal Cell Kinase	Protein Coding	53	Parkinson’s Disease	1	2711		0.87
TXNL1	Thioredoxin Like 1	Protein Coding	47	late-onset	1	2741		0.79
TMEM30A	Transmembrane Protein 30A	Protein Coding	46	Parkinson’s Disease, LRRK2	2	2895		0.62
RRAS	RAS Related	Protein Coding	52	LRRK2	1	2853		0.54
PRDM4	PR/SET Domain 4	Protein Coding	43	LRRK2	1	2853		0.54
NPTN	Neuroplastin	Protein Coding	49	neurodegeneration	1	2878		0.49
ATP6V1H	ATPase H+ Transporting V1 Subunit H	Protein Coding	48	LRRK2	1	2905		0.39
NME1	NME/NM23 Nucleoside Diphosphate Kinase 1	Protein Coding	57	neurodegeneration	1	2920		0.34
REEP5	Receptor Accessory Protein 5	Protein Coding	46	LRRK2	1	2936		0.28
SUCLA2	Succinate-CoA Ligase ADP-Forming Beta Subunit	Protein Coding	57	LRRK2	1	2936		0.28
BCAS2	BCAS2, Pre-MRNA Processing Factor	Protein Coding	46	LRRK2	1	2938		0.27
SF3A2	Splicing Factor 3a Subunit 2	Protein Coding	46	LRRK2	1	2938		0.27
HLTF	Helicase Like Transcription Factor	Protein Coding	48	LRRK2	1	2938		0.27
RAB22A	RAB22A, Member RAS Oncogene Family	Protein Coding	44	LRRK2	1	2938		0.27
RAN	RAN, Member RAS Oncogene Family	Protein Coding	54	neurodegeneration	1	2958		0.10
PTBP2	Polypyrimidine Tract Binding Protein 2	Protein Coding	47	neurodegeneration	1	2959		0.07

Indirect VarElect Results

Implicated Symbol	Implicating Symbol	Description	Category	GIFTs	Matched Phenotypes	Matched Phenotypes Count	Global Rank (Total Genes 11967)	Score (Implicated)
UBE2V2	PRKN	Parkin RBR E3 Ubiquitin Protein Ligase	Protein Coding	47	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	3	23.14
UBE2V2	RPS27A	Ribosomal Protein S27a	Protein Coding	53	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	45	23.14
UBE2V2	FBXO7	F-Box Protein 7	Protein Coding	50	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	16	23.14
UBE2V2	UCHL1	Ubiquitin C-Terminal Hydrolase L1	Protein Coding	62	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	7	23.14
UBE2V2	UBE2E2	Ubiquitin Conjugating Enzyme E2 E2	Protein Coding	49	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	145	23.14
SNAP91	SNCA	Synuclein Alpha	Protein Coding	61	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	2	13.14
SNAP91	PRKN	Parkin RBR E3 Ubiquitin Protein Ligase	Protein Coding	47	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	3	13.14
SNAP91	SYNJ1	Synaptojanin 1	Protein Coding	57	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	33	13.14
SNAP91	DNAJC6	DnaJ Heat Shock Protein Family (Hsp40) Member C6	Protein Coding	51	Parkinson’s Disease, LRRK2, late-onset	3	27	13.14
SNAP91	UCHL1	Ubiquitin C-Terminal Hydrolase L1	Protein Coding	62	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	7	13.14
TERF2IP	UCHL1	Ubiquitin C-Terminal Hydrolase L1	Protein Coding	62	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	7	12.55
TERF2IP	SNCA	Synuclein Alpha	Protein Coding	61	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	2	12.55
TERF2IP	AKT1	AKT Serine/Threonine Kinase 1	Protein Coding	65	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	79	12.55
TERF2IP	RPS27A	Ribosomal Protein S27a	Protein Coding	53	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	45	12.55
TERF2IP	TBP	TATA-Box Binding Protein	Protein Coding	57	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	40	12.55
PYCARD	BAX	BCL2 Associated X, Apoptosis Regulator	Protein Coding	57	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	107	9.78
PYCARD	NLRP3	NLR Family Pyrin Domain Containing 3	Protein Coding	56	Parkinson’s Disease, late-onset	2	222	9.78
PYCARD	PRKN	Parkin RBR E3 Ubiquitin Protein Ligase	Protein Coding	47	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	3	9.78
PYCARD	CASP3	Caspase 3	Protein Coding	61	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	56	9.78
PYCARD	SQSTM1	Sequestosome 1	Protein Coding	59	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	48	9.78
SRPRB	SNCA	Synuclein Alpha	Protein Coding	61	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	2	8.42
SRPRB	PRKN	Parkin RBR E3 Ubiquitin Protein Ligase	Protein Coding	47	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	3	8.42
SRPRB	RPS27A	Ribosomal Protein S27a	Protein Coding	53	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	45	8.42
SRPRB	UCHL1	Ubiquitin C-Terminal Hydrolase L1	Protein Coding	62	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	7	8.42
SRPRB	SNCAIP	Synuclein Alpha Interacting Protein	Protein Coding	52	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	8	8.42
EIF1B	EIF4G1	Eukaryotic Translation Initiation Factor 4 Gamma 1	Protein Coding	55	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	36	7.94
EIF1B	UCHL1	Ubiquitin C-Terminal Hydrolase L1	Protein Coding	62	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	7	7.94
EIF1B	RPS27A	Ribosomal Protein S27a	Protein Coding	53	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	45	7.94
EIF1B	SOD2	Superoxide Dismutase 2	Protein Coding	60	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	78	7.94
EIF1B	GAPDH	Glyceraldehyde-3-Phosphate Dehydrogenase	Protein Coding	58	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	98	7.94
MRPL15	PARK7	Parkinsonism Associated Deglycase	Protein Coding	56	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	5	7.78
MRPL15	RPS27A	Ribosomal Protein S27a	Protein Coding	53	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	45	7.78
MRPL15	NDUFS4	NADH:Ubiquinone Oxidoreductase Subunit S4	Protein Coding	52	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	31	7.78
MRPL15	TH	Tyrosine Hydroxylase	Protein Coding	62	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	24	7.78
MRPL15	EIF4G1	Eukaryotic Translation Initiation Factor 4 Gamma 1	Protein Coding	55	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	36	7.78
LYL1	GDNF	Glial Cell Derived Neurotrophic Factor	Protein Coding	59	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	52	7.74
LYL1	PLAU	Plasminogen Activator, Urokinase	Protein Coding	63	LRRK2, late-onset	2	162	7.74
LYL1	NFKB1	Nuclear Factor Kappa B Subunit 1	Protein Coding	63	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	409	7.74
LYL1	ZEB1	Zinc Finger E-Box Binding Homeobox 1	Protein Coding	57	late-onset	1	273	7.74
LYL1	BAX	BCL2 Associated X, Apoptosis Regulator	Protein Coding	57	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	107	7.74
XRCC2	RPS27A	Ribosomal Protein S27a	Protein Coding	53	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	45	6.43
XRCC2	NBN	Nibrin	Protein Coding	55	Parkinson’s Disease, late-onset, neurodegeneration	3	388	6.43
XRCC2	TP53	Tumor Protein P53	Protein Coding	65	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	201	6.43
XRCC2	COMT	Catechol-O-Methyltransferase	Protein Coding	61	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	34	6.43
XRCC2	ATM	ATM Serine/Threonine Kinase	Protein Coding	64	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	498	6.43
BMP8B	PLA2G6	Phospholipase A2 Group VI	Protein Coding	57	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	12	6.27
BMP8B	BDNF	Brain Derived Neurotrophic Factor	Protein Coding	59	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	42	6.27
BMP8B	MAPK14	Mitogen-Activated Protein Kinase 14	Protein Coding	62	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	89	6.27
BMP8B	IL10	Interleukin 10	Protein Coding	57	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	243	6.27
BMP8B	GDNF	Glial Cell Derived Neurotrophic Factor	Protein Coding	59	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	52	6.27
LAPTM4B	GBA	Glucosylceramidase Beta	Protein Coding	59	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	11	5.59
LAPTM4B	ABCB1	ATP Binding Cassette Subfamily B Member 1	Protein Coding	61	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	449	5.59
LAPTM4B	NEDD4	Neural Precursor Cell Expressed, Developmentally Down-Regulated 4, E3 Ubiquitin Protein Ligase	Protein Coding	55	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	669	5.59
LAPTM4B	SLC11A2	Solute Carrier Family 11 Member 2	Protein Coding	58	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	132	5.59
LAPTM4B	NFE2L2	Nuclear Factor, Erythroid 2 Like 2	Protein Coding	56	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	351	5.59
ADSS	GBA	Glucosylceramidase Beta	Protein Coding	59	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	11	4.39
ADSS	PLA2G6	Phospholipase A2 Group VI	Protein Coding	57	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	12	4.39
ADSS	GAD1	Glutamate Decarboxylase 1	Protein Coding	61	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	120	4.39
ADSS	APOE	Apolipoprotein E	Protein Coding	61	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	13	4.39
ADSS	MAPK14	Mitogen-Activated Protein Kinase 14	Protein Coding	62	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	89	4.39
BTBD3	CUL1	Cullin 1	Protein Coding	55	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	143	1.47
BTBD3	SKP1	S-Phase Kinase Associated Protein 1	Protein Coding	52	Parkinson’s Disease, LRRK2, neurodegeneration	3	310	1.47
BTBD3	E2F1	E2F Transcription Factor 1	Protein Coding	53	Parkinson’s Disease, LRRK2, neurodegeneration	3	1777	1.47
BTBD3	DAPK1	Death Associated Protein Kinase 1	Protein Coding	57	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	823	1.47
BTBD3	PLCB1	Phospholipase C Beta 1	Protein Coding	57	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	1019	1.47
RTCA	LMNB1	Lamin B1	Protein Coding	57	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	228	1.23
RTCA	NOP56	NOP56 Ribonucleoprotein	Protein Coding	51	Parkinson’s Disease, late-onset, neurodegeneration	3	373	1.23
RTCA	IL6	Interleukin 6	Protein Coding	59	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	197	1.23
RTCA	HNRNPA1	Heterogeneous Nuclear Ribonucleoprotein A1	Protein Coding	56	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	538	1.23
RTCA	TNF	Tumor Necrosis Factor	Protein Coding	63	Parkinson’s Disease, LRRK2, late-onset, neurodegeneration	4	185	1.23

EnrichR Enrichment

KEGG

Wikipathways

Reactome

GO Cellular Component

GO Biological Process

GO Molecular Function